Journal articles on the topic 'Smoke and fire detection'
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1
Dong, Wen-Hui, Xue-Er Sheng, Shu Wang, and Tian Deng. "Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection." Applied Sciences 13, no. 9 (April 30, 2023): 5592. http://dx.doi.org/10.3390/app13095592.
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Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1μm. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke.
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Lu, Xiaoman, Xiaoyang Zhang, Fangjun Li, Mark A. Cochrane, and Pubu Ciren. "Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions." Remote Sensing 13, no. 2 (January 8, 2021): 196. http://dx.doi.org/10.3390/rs13020196.
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Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.
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Lu, Xiaoman, Xiaoyang Zhang, Fangjun Li, Mark A. Cochrane, and Pubu Ciren. "Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions." Remote Sensing 13, no. 2 (January 8, 2021): 196. http://dx.doi.org/10.3390/rs13020196.
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Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.
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Peat, Bob. "Fire detection without smoke." Physics World 6, no. 6 (June 1993): 23–25. http://dx.doi.org/10.1088/2058-7058/6/6/18.
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Huang, Jingwen, Jiashun Zhou, Huizhou Yang, Yunfei Liu, and Han Liu. "A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection." Forests 14, no. 1 (January 16, 2023): 162. http://dx.doi.org/10.3390/f14010162.
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Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, APS (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects.
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Sun, Bingjian, Pengle Cheng, and Ying Huang. "Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning." Sensors 22, no. 21 (November 1, 2022): 8383. http://dx.doi.org/10.3390/s22218383.
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To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification.
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Bhamra, Jaspreet Kaur, Shreyas Anantha Ramaprasad, Siddhant Baldota, Shane Luna, Eugene Zen, Ravi Ramachandra, Harrison Kim, et al. "Multimodal Wildland Fire Smoke Detection." Remote Sensing 15, no. 11 (May 27, 2023): 2790. http://dx.doi.org/10.3390/rs15112790.
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Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires.
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Bashambu, Dr Shallu, Anupam Gupta, and Sarthak Khandelwal. "Real Time Fire and Smoke Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2593–600. http://dx.doi.org/10.22214/ijraset.2023.54039.
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Abstract: This research paper presents a real-time fire and smoke detection system using the YOLOv5 object detection algorithm. The system aims to detect fire and smoke in images and video streams captured by a camera in real-time, without the need for any preprocessing or manual intervention. The proposed system uses the YOLOv5 algorithm to detect the fire and smoke regions in the input images and videos. The YOLOv5 model is trained on a dataset of annotated images to recognize fire and smoke patterns accurately. The proposed system has been tested on different datasets and has achieved high accuracy and precision in detecting fire and smoke in real-time. The experimental results demonstrate that the proposed system is robust and efficient, and it can detect fire and smoke in real-time with high accuracy and low latency. The proposed system can be used in various applications, such as early warning systems, fire safety, and disaster management. It can also be integrated with the CCTV network directly
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Yang, Huanyu, Jun Wang, and Jiacun Wang. "Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning." Remote Sensing 15, no. 23 (November 27, 2023): 5527. http://dx.doi.org/10.3390/rs15235527.
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Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP50 and 57.3% AP50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance.
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Zheng, Xin, Feng Chen, Liming Lou, Pengle Cheng, and Ying Huang. "Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network." Remote Sensing 14, no. 3 (January 23, 2022): 536. http://dx.doi.org/10.3390/rs14030536.
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To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.
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Qian, Jingjing, Ji Lin, Di Bai, Renjie Xu, and Haifeng Lin. "Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model." Forests 14, no. 4 (April 19, 2023): 838. http://dx.doi.org/10.3390/f14040838.
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The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage of a wide diffusion range that is not easily obscured. Therefore, timely detection of forest fire smoke with better real-time detection for early warnings of forest fires wins valuable time for timely firefighting and also has great significance and applications for the development of forest fire detection systems. However, existing forest fire smoke detection methods still have problems, such as low detection accuracy, slow detection speed, and difficulty detecting smoke from small targets. In order to solve the aforementioned problems and further achieve higher accuracy in detection, this paper proposes an improved, new, high-accuracy forest fire detection model, the OBDS. Firstly, to address the problem of insufficient extraction of effective features of forest fire smoke in complex forest environments, this paper introduces the SimAM attention mechanism, which makes the model pay more attention to the feature information of forest fire smoke and suppresses the interference of non-targeted background information. Moreover, this paper introduces Omni-Dimensional Dynamic Convolution instead of static convolution and adaptively and dynamically adjusts the weights of the convolution kernel, which enables the network to better extract the key features of forest fire smoke of different shapes and sizes. In addition, to address the problem that traditional convolutional neural networks are not capable of capturing global forest fire smoke feature information, this paper introduces the Bottleneck Transformer Net (BoTNet) to fully extract global feature information and local feature information of forest fire smoke images while improving the accuracy of small target forest fire target detection of smoke, effectively reducing the model’s computation, and improving the detection speed of model forest fire smoke. Finally, this paper introduces the decoupling head to further improve the detection accuracy of forest fire smoke and speed up the convergence of the model. Our experimental results show that the model OBDS for forest fire smoke detection proposed in this paper is significantly better than the mainstream model, with a computational complexity of 21.5 GFLOPs (giga floating-point operations per second), an improvement of 4.31% compared with the YOLOv5 (YOLO, you only look once) model mAP@0.5, reaching 92.10%, and an FPS (frames per second) of 54, which is conducive to the realization of early warning of forest fires.
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Pan, Jin, Xiaoming Ou, and Liang Xu. "A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN." Forests 12, no. 6 (June 10, 2021): 768. http://dx.doi.org/10.3390/f12060768.
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Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.
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rajendra rao shinde, Rajshree, Shruti Sable, Kartik masalkar, Harshad Gaulker, Shailesh nardwar, Tejashri shinde, and J. B. fulzale. "HOSPITAL EMERGENCY SECURITY SYSTEM." International Journal of Engineering Applied Sciences and Technology 7, no. 1 (May 1, 2022): 264–67. http://dx.doi.org/10.33564/ijeast.2022.v07i01.038.
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The fire detection system combines the simultaneous measurements of smoke, carbon monoxide, and carbon dioxide. The security of campus against intruders moving in laboratories, class rooms, staffrooms or washrooms. The fire alarm system consists of Fire detectors (with can be smoke detector, heat, or Infra-Red detectors), control unit and alarm system. A fire detection system is developed based on the simultaneous measurements of temperature and smoke. The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone. Previous fire detection algorithms used data from sensors for temperature, smoke, and combustion products. The smoke sensor alarms when the analog output signal exceeds or equal the threshold value. The node includes analog sensors to measure smoke, carbon monoxide (CO) and temperature. A fire alarm system should reliably and in a timely way notify building occupants about the presence of fire indicators, such as smoke or high temperatures.
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Hong, Ter-Ki, and Seul-Hyun Park. "Effects of Optical Properties of Smoke Particles on Fire Detection Characteristics predicted by a Fire Dynamic Simulator Model." International Journal of Fire Science and Engineering 36, no. 4 (December 31, 2022): 45–55. http://dx.doi.org/10.7731/kifse.96827602.
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This study examined the smoke detection time prediction performance of the Fire Dynamics Simulator (FDS) in relation to the optical properties of smoke particles. Specifically, this work explored how smoke detection times are affected by the mass-specific light extinction coefficient, an input value needed to predict FDS smoke detection times. Therefore, a smoke generator and a smoke detection sensor that employs the light extinction principle were installed in a fire compartment, with the mass-specific light extinction coefficients of the smoke particles generated by the smoke generator measured through Gravimetric Sampling and Light Extinction (GSLE) experiments. FDS fire simulations were performed under the same conditions as the smoke detection experiments to compare the Optical Per Meter (OPM) of the detector. The results confirmed that the F DS fire simulation was consistent with the smoke detection experiments when the measured mass-specific light extinction coefficient of the smoke particles was entered as input. In addition, it was found that fluctuations in the mass-specific light extinction coefficient, an optical property of smoke particles, may significantly affect smoke detection times by directly affecting the OPM.
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Ngoc, Pham Van Bach, Le Huy Hoang, Le Minh Hieu, Ngoc Hai Nguyen, Nguyen Luong Thien, and Van Tuan Doan. "Real-Time Fire and Smoke Detection for Trajectory Planning and Navigation of a Mobile Robot." Engineering, Technology & Applied Science Research 13, no. 5 (October 13, 2023): 11843–49. http://dx.doi.org/10.48084/etasr.6252.
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Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.
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Gonçalves, Leon Augusto Okida, Rafik Ghali, and Moulay A. Akhloufi. "YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images." Fire 7, no. 4 (April 14, 2024): 140. http://dx.doi.org/10.3390/fire7040140.
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Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare.
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Trinath Basu, M., Ragipati Karthik, J. Mahitha, and V. Lokesh Reddy. "IoT based forest fire detection system." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 124. http://dx.doi.org/10.14419/ijet.v7i2.7.10277.
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It has been found in a survey that 80% losses caused due to fire would have been kept away from if the fire was identified promptly. Node Mcu based IoT empowered fire indicator and observing framework is the answer for this issue.In this task, we have assembled fire finder utilizing Node Mcu which is interfaced with a temperature sensor, a smoke sensor and signal. The temperature sensor detects the warmth and smoke senssor detects any smoke produced because of consuming or fire. buzzer associated with Arduino gives us an alert sign. At whatever point fire activated, it consumes protests adjacent and produces smoke. A fire caution can likewise be activated because of little smoke from candlelight or oil lights utilized as a part of a family. Likewise, at whatever point warm force is high then additionally the alert goes on. Bell or alert is killed at whatever point the temperature goes to ordinary room temperature and smoke level decreases. We have additionally interfaced LCD show to the Node Mcu board.With the assistance of IoT innovation.Node MCU fire checking serves for mechanical need and also for family unit reason. At whatever point it recognizes fire or smoke then it immediately alarms the client about the fire through the ethernet module. For this reason, we are utilizing ESP8266 which is from Arduino IDE. Likewise, the Node Mcu interfacing with LCD show is done to show the status of the framework whether the Smoke and Overheat is identified or not. What's more, Node Mcu interfacing with Ethernet module is done as such that client become more acquainted with about the predominant condition message. It insinuate the client about the fire identification. This framework is extremely helpful at whatever point the client isn't in the closeness of control focus. At whatever point a fire happens, the framework naturally faculties and alarms the client by sending an alarm to an application introduced on user’s Android portable or page open through web.
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Sawant, Swapnil, Samarth Kumbhar, Bhakti Chauhan, Gaurav Chaudhari, and Prachi Thakkar. "Integrated Fire Detection System using ML and IOT." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1738–41. http://dx.doi.org/10.22214/ijraset.2024.60063.
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Abstract: An integrated fire detection system that combines smoke, thermal, and image analysis using machine learning can provide real-time fire detection and reduce false alarms, thereby enhancing safety. Traditional single-sensor fire detection systems that rely on smoke or heat often result in false alarms or delays. The proposed multi-model system involves combining smoke, thermal, and YOLO v8 image detection technologies. However, challenges such as data privacy, cost, and maintenance need to be addressed. Nevertheless, this advanced fire detection system has the potential to significantly improve fire protection.
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Sawant, Swapnil. "Integrated Fire Detection System using ML and IOT." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 2091–100. http://dx.doi.org/10.22214/ijraset.2024.61810.
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Abstract: An integrated fire detection system that combines smoke, thermal, and image analysis using machine learning can provide real-time fire detection and reduce false alarms, thereby enhancing safety. Traditional single-sensor fire detection systems that rely on smoke or heat often result in false alarms or delays. The proposed multi-model system involves combining smoke, thermal, and YOLO v8 image detection technologies. However, challenges such as data privacy, cost, and maintenance need to be addressed. Nevertheless, this advanced fire detection system has the potential to significantly improve fire protection.
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Chetoui, Mohamed, and Moulay A. Akhloufi. "Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models." Fire 7, no. 4 (April 12, 2024): 135. http://dx.doi.org/10.3390/fire7040135.
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Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry.
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Vinay, Kumar Jain, and Jain Chitrangad. "Fire and smoke detection using YOLOv8." i-manager's Journal on Artificial Intelligence & Machine Learning 1, no. 2 (2023): 22. http://dx.doi.org/10.26634/jaim.1.2.19849.
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In smart cities, fire can have disastrous effects, destroying property and putting residents' lives in danger, making it difficult to identify fire in real time because of the accuracy and speed constraints of traditional fire detection techniques. To address this issue, an accurate and cost-effective system that can be used in almost any fire detection scenario was developed. A CNN was used to analyze live video from a fire monitoring system to identify fire. An object identification model for deep learning called You Only Look Once (YOLOv8) was used to detect fire. To identify and alert videos from CCTV footage, a dataset of video frames with flames is used. After pre-processing the data, CNN is used to build a Machine Learning (ML) model. The methodology adopted in this study demonstrated the ability to adjust to various situations.
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Aleshkov, M. V., S. V. Popov, N. G. Topolskiy, A. V. Mokshantsev, K. A. Mikhaylov, D. S. Afanasov, K. N. Samsonov, and L. A. Iftodi. "Results of tests for the detection of a fire source using infrared measuring instruments." Technology of technosphere safety 93 (2021): 19–28. http://dx.doi.org/10.25257/tts.2021.3.93.19-28.
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Introduction. The Academy of State Fire Service of EMERCOM of Russia conducted tests of technical means of measuring the infrared range for detecting a fire source in the mobile firing range of the PTS "Lava". The results of tests for detecting both a fire source and a person in conditions of reduced visibility - in conditions of artificial smoke are presented. Goals and objectives. The purpose of the study is to determine the effectiveness of technical means of measuring the infrared range when training fire departments in conditions of reduced (zero) visibility. The objective of the study is to determine the source of ignition and the person in the artificial smoke by technical means of measuring the infrared range and analyzing the results obtained. Methods. When conducting a study on the detection of a fire source in the conditions of artificial smoke, scientific methods were used: analysis, synthesis and full-scale experiment. Results and their discussion. The results of tests at the PTS "Lava" firing range showed that when using a short-wave infrared camera, the detection range of both a fire source and a person in artificial smoke is five times greater than when using a visible range camera. The obtained results can be used in the educational process when preparing the personnel of fire departments for practicing professional skills. Conclusions. As a result of an experimental study on the detection of a fire source and a person in artificial smoke, the effectiveness of using a camera of the short-wave infrared range (SWIR-range) by fire departments when working in an environment unsuitable for breathing has been proved. Keywords: visibility, smoke, swir-range of the electromagnetic spectrum, fire source
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Yin, Hang, Yurong Wei, Hedan Liu, Shuangyin Liu, Chuanyun Liu, and Yacui Gao. "Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection." Complexity 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/6843869.
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Real-time smoke detection is of great significance for early warning of fire, which can avoid the serious loss caused by fire. Detecting smoke in actual scenes is still a challenging task due to large variance of smoke color, texture, and shapes. Moreover, the smoke detection in the actual scene is faced with the difficulties in data collection and insufficient smoke datasets, and the smoke morphology is susceptible to environmental influences. To improve the performance of smoke detection and solve the problem of too few datasets in real scenes, this paper proposes a model that combines a deep convolutional generative adversarial network and a convolutional neural network (DCG-CNN) to extract smoke features and detection. The vibe algorithm was used to collect smoke and nonsmoke images in the dynamic scene and deep convolutional generative adversarial network (DCGAN) used these images to generate images that are as realistic as possible. Besides, we designed an improved convolutional neural network (CNN) model for extracting smoke features and smoke detection. The experimental results show that the method has a good detection performance on the smoke generated in the actual scenes and effectively reduces the false alarm rate.
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Wu, Xuehui, Xiaobo Lu, and Henry Leung. "A Video Based Fire Smoke Detection Using Robust AdaBoost." Sensors 18, no. 11 (November 5, 2018): 3780. http://dx.doi.org/10.3390/s18113780.
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This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.
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Hong, Ter-Ki, and Seul-Hyun Park. "Numerical Analysis of Smoke Behavior and Detection of Solid Combustible Fire Developed in Manned Exploration Module Based on Exploration Gravity." Fire 4, no. 4 (November 19, 2021): 85. http://dx.doi.org/10.3390/fire4040085.
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A fire during manned space exploration can cause serious casualties and disrupt the mission if the initial response is delayed. Therefore, measurement technology that can detect fire in the early stage of ignition is important. There have been a number of works that investigate the smoke behaviors in microgravity as the foundation for a reliable method for sensing a fire during spaceflight. For space missions to the outer planets, however, a strategy of detecting smoke as an indicator of fire should be adjusted to cover the fire scenario that can be greatly affected by changes in gravity (microgravity, lunar, Mars, and Earth gravity). Therefore, as a preliminary study on fire detectors of the manned pressurized module, the present study examined the smoke particle behavior and detection characteristics with respect to changes in gravity using numerical analysis. In particular, the effects of the combination of buoyancy and ventilation flow on the smoke particle movement pattern was investigated to further improve the understanding of the fire detection characteristics of the smoke detector, assuming that a fire occurred in different gravity environments inside the pressurized module. To this end, we modeled the internal shape of Destiny and performed a series of numerical analysis using the Fire Dynamics Simulator (FDS). The findings of this study can provide basic data for the design of a fire detection system for manned space exploration modules.
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Hossain, F. M. Anim, Youmin M. Zhang, and Masuda Akter Tonima. "Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern." Journal of Unmanned Vehicle Systems 8, no. 4 (December 1, 2020): 285–309. http://dx.doi.org/10.1139/juvs-2020-0009.
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In recent years, the frequency and severity of forest fire occurrence have increased, compelling the research communities to actively search for early forest fire detection and suppression methods. Remote sensing using computer vision techniques can provide early detection from a large field of view along with providing additional information such as location and severity of the fire. Over the last few years, the feasibility of forest fire detection by combining computer vision and aerial platforms such as manned and unmanned aerial vehicles, especially low cost and small-size unmanned aerial vehicles, have been experimented with and have shown promise by providing detection, geolocation, and fire characteristic information. This paper adds to the existing research by proposing a novel method of detecting forest fire using color and multi-color space local binary pattern of both flame and smoke signatures and a single artificial neural network. The training and evaluation images in this paper have been mostly obtained from aerial platforms with challenging circumstances such as minuscule flame pixels, varying illumination and range, complex backgrounds, occluded flame and smoke regions, and smoke blending into the background. The proposed method has achieved F1 scores of 0.84 for flame and 0.90 for smoke while maintaining a processing speed of 19 frames per second. It has outperformed support vector machine, random forest, Bayesian classifiers and YOLOv3, and has demonstrated the capability of detecting challenging flame and smoke regions of a wide range of sizes, colors, textures, and opacity.
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Ding, Yunhong, Mingyang Wang, Yujia Fu, and Qian Wang. "Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds." Forests 15, no. 5 (May 10, 2024): 839. http://dx.doi.org/10.3390/f15050839.
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Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.
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Sun, Long, Yidan Li, and Tongxin Hu. "ForestFireDetector: Expanding Channel Depth for Fine-Grained Feature Learning in Forest Fire Smoke Detection." Forests 14, no. 11 (October 30, 2023): 2157. http://dx.doi.org/10.3390/f14112157.
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Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being and economic stability. In light of the problems of noise misclassification and manual design of the components in the current forest fire detection model, particularly the limited capability to identify subtle and unnoticeable smoke within intricate forest environments, this paper proposes an improved smoke detection model for forest fires utilizing YOLOv8 as its foundation. We expand the channel depth for fine-grain feature learning and retain more feature information. At the same time, lightweight convolution reduces the parameters of the model. This model enhances detection accuracy for smoke targets of varying scales and surpasses the accuracy of mainstream models. The outcomes of experiments demonstrate that the improved model exhibits superior performance, and the mean average precision is improved by 3.3%. This model significantly enhances the detection ability while also optimizing the neural network to make it more lightweight. These advancements position the model as a promising solution for early-stage forest fire smoke detection.
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Sinha, Manya, Shivank Solanki, Sudeep Batra, Dr Yojna Arora, and Abhishek Tewatia. "Fire Alarm System Through Smoke Detection." International Journal of Innovative Research in Computer Science and Technology 11, no. 4 (July 1, 2023): 01–04. http://dx.doi.org/10.55524/ijircst.2023.11.4.1.
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A reliable fire alarm system (FAS) is crucial for timely reporting and responding to fires. While existing techniques can predict undesirable outcomes, they lack guidance on when and how workers should intervene to minimize the associated costs. Recent advancements in sensors, microelectronics, and information technology have significantly improved fire detection technologies. However, the prevalence of synthetic materials in modern homes has increased the danger of fire-related injuries and deaths due to the release of toxic fumes and gases, including carbon monoxide. This highlights the need for ongoing analysis and development of fire detection techniques to ensure the safety of occupants.
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Kalmykov, Sergey Petrovich, and Viktor Nikolaevich Tokarev. "Influence of room height on estimated fire detection time." Technology of technosphere safety, no. 100 (2023): 100–113. http://dx.doi.org/10.25257/tts.2023.2.100.100-113.
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Introduction. Within the framework of this article, a quantitative and qualitative assessment of the influence of the height of the room on the estimated time of fire detection was carried out. Purpose. Assessment of the effect of the room height on the estimated fire detection time. Methods. Using the Fire Dynamics Simulator software system used to simulate the fire process, numerical experiments were carried out to study the fire detection time, which is considered in this article as an interval from the moment of fire start to reaching the values of optical density of smoke, at which point smoke fire detectors are triggered. Results and their discussion. As a result of numerical modeling of fire in rooms of different heights, data on the change in the optical density of smoke in the initial stage of fire are established. Based on the information received, the estimated time of fire detection by automatic fire alarm was determined according to the algorithms of triggering A and B at different values of permissible sensitivity for point smoke fire detectors, taking into account the height of the room. The results of numerical experiments can be used for expert assessment of the design time of fire detection as a "technical" component of the evacuation start time taken into account when determining the individual fire risk and conditions for safe evacuation of people. Conclusions. It was found that the height of the room has a slight impact on the estimated fire detection time. However, this effect is manifested in the case of the operation of point smoke detectors at the upper threshold of permissible sensitivity. To take into account the time of fire detection as part of the evacuation start time, further study of this issue is required with comparison of modeling results with the results of field experiments. Keywords: optical density of smoke; fire alarm; evacuation start time; fire detection time.
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Zhao, Liang, Jixue Liu, Stefan Peters, Jiuyong Li, Simon Oliver, and Norman Mueller. "Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model." Remote Sensing 14, no. 13 (June 25, 2022): 3047. http://dx.doi.org/10.3390/rs14133047.
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Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely “Smoke”, “Clear”, and “Other_aerosol”. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named “Variant Input Bands for Smoke Detection (VIB_SD)”, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.
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Choi, Su-Gil, Yoo-Jeong Choi, Yeong-Jae Nam, and Si-Kuk Kim. "Fire Detection Tendency through Combustion Products Generated during UL 268 Wood Flame Fire and Smoldering Fire Test." Fire Science and Engineering 35, no. 1 (February 28, 2021): 48–57. http://dx.doi.org/10.7731/kifse.23b37311.
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This experiment analyzes the tendency of fire detection through combustion products generated during UL 268 wood flame fires and smoldering tests. Fire detection tendency was measured using a particle matter sencor (PMS), combustion gas analyzer (CGA), and gas analyzer (GA). The combustion products were matched and analyzed at 5 %/m (non-operation), 10 %/m, and 15 %/m of the smoke sensitivity measured by the smoke detector. In the case of wood flaming fire, PMS PM 10, CGA CO, SO2, GA HCHO, and TVOC, the trend was observed because of the continuous increase in the measured value according to the smoke generation. In the case of smoldering, PM 10, CO, and HCHO were adaptable to the tendency to be observed. Finally, in the case of wood fire accompanied by flame fire and smoldering to PM 10, CO and HCHO were considered to be the optimal fire detection factors.
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Ho, Chao-Ching. "Nighttime Fire/Smoke Detection System Based on a Support Vector Machine." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/428545.
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Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.
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Yin, Hang, Mingxuan Chen, Wenting Fan, Yuxuan Jin, Shahbaz Gul Hassan, and Shuangyin Liu. "Efficient Smoke Detection Based on YOLO v5s." Mathematics 10, no. 19 (September 25, 2022): 3493. http://dx.doi.org/10.3390/math10193493.
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Smoke detection based on video surveillance is important for early fire warning. Because the smoke is often small and thin in the early stage of a fire, using the collected smoke images for the identification and early warning of fires is very difficult. Therefore, an improved lightweight network that combines the attention mechanism and the improved upsampling algorithm has been proposed to solve the problem of small and thin smoke in the early fire stage. Firstly, the dataset consists of self-created small and thin smoke pictures and public smoke pictures. Secondly, an attention mechanism module combined with channel and spatial attention, which are attributes of pictures, is proposed to solve the small and thin smoke detection problem. Thirdly, to increase the receptive field of the smoke feature map in the feature fusion network and to solve the problem caused by the different smoke scenes, the original upsampling has been replaced with an improved upsampling algorithm. Finally, extensive comparative experiments on the dataset show that improved detection model has demonstrated an excellent effect.
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Sun, Baoshan, Kaiyu Bi, and Qiuyan Wang. "YOLOv7-FIRE: A tiny-fire identification and detection method applied on UAV." AIMS Mathematics 9, no. 5 (2024): 10775–801. http://dx.doi.org/10.3934/math.2024526.
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<abstract> <p>Fire is a common but serious disaster, which poses a great threat to human life and property. Therefore, fire-smoke detection technology is of great significance in various fields. In order to improve the detection ability of tiny-fire, so as to realize the prediction and suppression of fire as soon as possible, we proposed an efficient and accurate tiny-fire detection method based on the optimized YOLOv7, and we named the improved model YOLOv7-FIRE. First, we introduced the BiFormer into YOLOv7 to make the network pay more attention to the fire-smoke area. Second, we introduced the NWD technique to enhance the perception of the algorithm for small targets, and provided richer semantic information by modeling the context information around the target. Finally, CARAFE was applied for content-aware feature reorganization, which preserved the details and texture information in the image and improved the quality of fire-smoke detection. Furthermore, in order to improve the robustness of the improved algorithm, we expanded the fire-smoke dataset. The experimental results showed that YOLOv7-FIRE as significantly better than the previous algorithm in detection accuracy and recall rate, the Precision increased from 75.83% to 82.31%, and the Recall increased from 66.43% to 74.02%.</p> </abstract>
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Feng, Xuhong, Pengle Cheng, Feng Chen, and Ying Huang. "Full-Scale Fire Smoke Root Detection Based on Connected Particles." Sensors 22, no. 18 (September 7, 2022): 6748. http://dx.doi.org/10.3390/s22186748.
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Smoke is an early visual phenomenon of forest fires, and the timely detection of smoke is of great significance for early warning systems. However, most existing smoke detection algorithms have varying levels of accuracy over different distances. This paper proposes a new smoke root detection algorithm that integrates the static and dynamic features of smoke and detects the final smoke root based on clustering and the circumcircle. Compared with the existing methods, the newly developed method has a higher accuracy and detection efficiency on the full scale, indicating that the method has a wider range of applications in the quicker detection of smoke in forests and the prevention of potential forest fire spread.
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Xiong, Ding, and Lu Yan. "Early smoke detection of forest fires based on SVM image segmentation." Journal of Forest Science 65, No. 4 (April 26, 2019): 150–59. http://dx.doi.org/10.17221/82/2018-jfs.
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A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.
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Roh, Joohyung, Yukyung Kim, and Minsuk Kong. "Fire Image Classification Based on Convolutional Neural Network for Smart Fire Detection." International Journal of Fire Science and Engineering 36, no. 3 (September 30, 2022): 51–61. http://dx.doi.org/10.7731/kifse.cb750817.
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This study investigated the effect of the class number on the prediction performance of the convolutional neural network (CNN) classification model that is applied in fire detectors to reduce nuisance fire alarms by appropriately recognizing fire images including those of flames and smoke. A CNN model trained by transfer learning using five image datasets of flame, smoke, normal, haze, and light was realized and trained by altering the class number to generate the classification model. A total of three classification models were generated as follows: classification model 1 was trained using normal and fire images including flames and smoke; classification model 2 was trained using flame, smoke, and normal images; and classification model 3 was trained using flames, smoke, normal, and haze, and light images. A test image dataset independent of training was used to assess the prediction performance of the three classification models. The results indicate that the prediction accuracy for classification models 1, 2, and 3 were approximately 93.0%, 94.2%, and 97.3%, respectively. The performance of the predicted classification improved as the class number increased, because the model could learn with greater precision the features of the normal images that are similar to those of the fire images.
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Han, Ziqi, Ye Tian, Change Zheng, and Fengjun Zhao. "Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion." Forests 15, no. 4 (April 11, 2024): 689. http://dx.doi.org/10.3390/f15040689.
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The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained.
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Belure, Prasad, Chirag Bhagat, Om Bhadane, Girija Bendale, Shourya Bhade, Chetan Bhagat, and Vaishali Saval. "Low Power Forest Fire Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2498–500. http://dx.doi.org/10.22214/ijraset.2023.51967.
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Abstract: Fire detection and warning systems are critical for ensuring the safety of people and property. We present an innovative system for detecting and alerting fires that employs a combination of an MQ135 gas sensor, Arduino Uno microcontroller, and SIM900A GSM module. Its purpose is to identify the existence of smoke and other types of gases that are emitted during a fire and to provide real-time alerts to a pre-programmed set of phone numbers through SMS using the SIM900A GSM module. The system also triggers an alarm to alert people and the forest department in the vicinity of the fire. The MQ135 gas sensor is used to detect the presence of smoke and other gases in the air. The sensor's output is fed into the Arduino Uno microcontroller, which processes the data and triggers the alarm if the concentration of smoke or other gases exceeds a pre-set threshold value. The SIM900A GSM module is used to send real-time alerts to pre-programmed phone numbers through SMS, enabling swift action to be taken in case of a fire. We also use deep sleep mode to save power. We have divided the forest into various zones according to activity in that area.
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Wang, Zewei, Change Zheng, Jiyan Yin, Ye Tian, and Wenbin Cui. "A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting." Electronics 10, no. 21 (October 31, 2021): 2675. http://dx.doi.org/10.3390/electronics10212675.
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Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.
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Utkin, Andrei B., Armando Fernandes, Fernando Simões, Alexander Lavrov, and Rui Vilar. "Feasibility of forest-fire smoke detection using lidar." International Journal of Wildland Fire 12, no. 2 (2003): 159. http://dx.doi.org/10.1071/wf02048.
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The feasibility and fundamentals of forest fire detection by smoke sensing with single-wavelength lidar are discussed with reference to results of 532-nm lidar measurements of smoke plumes from experimental forest fires in Portugal within the scope of the Gestosa 2001 project. The investigations included tracing smoke-plume evolution, estimating forest-fire alarm promptness, and smoke-plume location by azimuth rastering of the lidar optical axis. The possibility of locating a smoke plume whose source is out of line of sight and detection under extremely unfavourable visibility conditions was also demonstrated. The eye hazard problem is addressed and three possibilities of providing eye-safety conditions without loss of lidar sensitivity (namely, using a low energy-per-pulse and high repetition-rate laser, an expanded laser beam, or eye-safe radiation) are discussed.
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Li, Tingting, Changchun Zhang, Haowei Zhu, and Junguo Zhang. "Adversarial Fusion Network for Forest Fire Smoke Detection." Forests 13, no. 3 (February 22, 2022): 366. http://dx.doi.org/10.3390/f13030366.
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Recent advances suggest that deep learning has been widely used to detect smoke for early forest fire warnings. Despite its remarkable success, this approach has a number of problems in real life application. Deep neural networks only learn deep and abstract representations, while ignoring shallow and detailed representations. In addition, previous models have been trained on source domains but have generalized weakly on unseen domains. To cope with these problems, in this paper, we propose an adversarial fusion network (AFN), including a feature fusion network and an adversarial feature-adaptation network for forest fire smoke detection. Specifically, the feature fusion network is able to learn more discriminative representations by fusing abstract and detailed features. Meanwhile, the adversarial feature adaptation network is employed to improve the generalization ability and transfer gains of the AFN. Comprehensive experiments on two self-built forest fire smoke datasets, and three publicly available smoke datasets, validate that our method significantly improves the performance and generalization of smoke detection, particularly the accuracy of the detection of small amounts of smoke.
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Lee, Yeunghak, and Jaechang Shim. "False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning." Electronics 8, no. 10 (October 15, 2019): 1167. http://dx.doi.org/10.3390/electronics8101167.
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Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.
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Mikolai, Imrich, and Ján Tkáč. "Firefighting Systems and Video Smoke Detection as a Significant Part of Fire Safety in the Building." Advanced Materials Research 1057 (October 2014): 196–203. http://dx.doi.org/10.4028/www.scientific.net/amr.1057.196.
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Full functionally fire safety in the building is very important and significant factor of well designed, built and used building. The safety of persons in the building increases except passive fire protection (fire splitting building by fire protection structure) also by proposition and realization of active firefighting equipment; which are not designed only for detection of fire, but also for fire localization and people security against hot of fire and against combustion products and smoke. Lately, the phenomenon of human life protection begins consistently monitored in the building. Major factor in the monitoring of fire and smoke is video smoke detection.
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Deng, Li, Qian Chen, Yuanhua He, Xiubao Sui, and Qin Wang. "Detection of smoke from infrared image frames in the aircraft cargoes." International Journal of Distributed Sensor Networks 17, no. 4 (April 2021): 155014772110098. http://dx.doi.org/10.1177/15501477211009808.
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The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s statistics, the false alarm rate is as high as 99%. Since, in the cargo of civil aircraft, visible image processing technology cannot be used to detect smoke in the event of a fire due to the closed dark environment, a novel smoke detection method using infrared image processing technology is presented. Experiments were conducted under different environment pressures in the full-size cargo of civil aircraft. The results show that the proposed method can effectively detect smoke at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.
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Chen, Xin, Yipeng Xue, Qingshan Hou, Yan Fu, and Yaolin Zhu. "RepVGG-YOLOv7: A Modified YOLOv7 for Fire Smoke Detection." Fire 6, no. 10 (October 7, 2023): 383. http://dx.doi.org/10.3390/fire6100383.
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To further improve the detection of smoke and small target smoke in complex backgrounds, a novel smoke detection model called RepVGG-YOLOv7 is proposed in this paper. Firstly, the ECA attention mechanism and SIoU loss function are applied to the YOLOv7 network. The network effectively extracts the feature information of small targets and targets in complex backgrounds. Also, it makes the convergence of the loss function more stable and improves the regression accuracy. Secondly, RepVGG is added to the YOLOv7 backbone network to enhance the ability of the model to extract features in the training phase, while achieving lossless compression of the model in the inference phase. Finally, an improved non-maximal suppression algorithm is used to improve the detection in the case of dense smoke. Numerical experiments show that the detection accuracy of the proposed algorithm can reach about 95.1%, which contributes to smoke detection in complex backgrounds and small target smoke.
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Wang, Aoran, Guanghao Liang, Xuan Wang, and Yongchao Song. "Application of the YOLOv6 Combining CBAM and CIoU in Forest Fire and Smoke Detection." Forests 14, no. 11 (November 17, 2023): 2261. http://dx.doi.org/10.3390/f14112261.
Full textAbstract:
Forest fires are a vulnerable and devastating disaster that pose a major threat to human property and life. Smoke is easier to detect than flames due to the vastness of the wildland scene and the obscuring vegetation. However, the shape of wind-blown smoke is constantly changing, and the color of smoke varies greatly from one combustion chamber to another. Therefore, the widely used sensor-based smoke and fire detection systems have the disadvantages of untimely detection and a high false detection rate in the middle of an open environment. Deep learning-based smoke and fire object detection can recognize objects in the form of video streams and images in milliseconds. To this end, this paper innovatively employs CBAM based on YOLOv6 to increase the extraction of smoke and fire features. In addition, the CIoU loss function was used to ensure that training time is reduced while extracting the feature effects. Automatic mixed-accuracy training is used to train the model. The proposed model has been validated on a self-built dataset containing multiple scenes. The experiments demonstrated that our model has a high response speed and accuracy in real-field smoke and fire detection, which provides intelligent support for forest fire safety work in social life.
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Hamzah, Shipun Anuar, Mohd Noh Dalimin, Mohamad Md Som, Mohd Shamian Zainal, Khairun Nidzam Ramli, Wahyu Mulyo Utomo, and Nor Azizi Yusoff. "High accuracy sensor nodes for a peat swamp forest fire detection using ESP32 camera." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 3 (December 1, 2022): 229. http://dx.doi.org/10.11591/ijict.v11i3.pp229-239.
Full textAbstract:
<span>The </span><span lang="MS">use of smoke sensors in high-precision and low-cost forest fire detection kits needs to be developed immediately to assist the authorities in monitoring forest fires especially in remote areas more efficiently and systematically. The implementation of automatic reclosing operation allows the fire detector kit to distinguish between real smoke and non-real smoke successfully. This has profitably reduced kit errors when detecting fires and in turn prevent the users from receiving incorrect messages. However, using a smoke sensor with automatic reclosing operation has not been able to optimize the accuracy of identifying the actual smoke due to the working sensor node situation is difficult to predict and sometimes unexpected such as the source of smoke received. Thus, to further improve the accuracy when detecting the presence of smoke, the system is equipped with two digital cameras that can capture and send pictures of fire smoke to the users. The system gives the users choice of three interesting options if they want the camera to capture and send pictures to them, namely request, smoke trigger and movement for security purposes. In all cases, users can request the system to send pictures at any time. The system equipped with this camera shows the accuracy of smoke detection by confirming the actual smoke that has been detected through images sent in the user’s Telegram channel and on the Graphical User Interface (GUI) display. As a comparison of the system before and after using this camera, it was found that the system that uses the camera gives advantage to the users in monitoring fire smoke more effectively and accurately.</span>
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Bi, Zhen Bo, and Hua Yang. "Fire Image Detection System Based on Cloud Workflow." Applied Mechanics and Materials 678 (October 2014): 174–79. http://dx.doi.org/10.4028/www.scientific.net/amm.678.174.
Full textAbstract:
Fire image detection is a kind of important means of early prevention of fire. The current fire image detection is focus on the feature extraction, the concrete algorithm, etc., there is the lack of unified scheduling and the realization of automatic mechanism in the feature extraction of smoke or flame, the integration of all stages and other aspects of cohesion, and there is no uniform model in terms of integration. In addition, the traditional fire image detection system is facing problems such as high investment and insufficient computing capacity. To effectively reduce the detecting and early warning time, in this paper, fire image detection system based on the cloud workflow is studied. In view of the smoke image detection, we have integrated the various key of discrete nodes in fire detection through cloud workflow technology, and connect effectively the other related nodes. By building cloud workflow architecture, we organize and ensure the operation and regulation of the key nodes and their child nodes of child nodes. Through the experimental test, the method reduce the time in the whole process of fire detection, also won't reduce the accuracy of the original mode of fire detection.