Articoli di riviste: "Detection and recognition"

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Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, Shunsuke Yamaji, Tamotsu Kitabayashi, Shigeki Matsuda, Takayoshi Yamada e Ning Xu. "Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients". Detection 01, n. 01 (2013): 1–6. http://dx.doi.org/10.4236/detection.2013.11001.

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Shah, Dr Dipti M., e Parul D. Sindha. "Color detection in real time traffic sign detection and recognition system". Indian Journal of Applied Research 3, n. 7 (1 ottobre 2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.

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Srilatha, J., T. S. Subashini e K. Vaidehi. "Solid Waste Detection and Recognition using Faster RCNN". Indian Journal Of Science And Technology 16, n. 42 (13 novembre 2023): 3778–85. http://dx.doi.org/10.17485/ijst/v16i42.2005.

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Shevtekar, Prof Sumit, e Shrinidhi kulkarni. "Traffic-sign Recognition and Detection using Yolo-v8". International Journal of Research Publication and Reviews 5, n. 5 (2 maggio 2024): 1619–31. http://dx.doi.org/10.55248/gengpi.5.0524.1141.

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Yamini, Maidam. "Number Plate Detection in an Image". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n. 09 (1 settembre 2023): 1–11. http://dx.doi.org/10.55041/ijsrem25883.

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Abstract (sommario):

Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this area. We propose a unified convolutional neural network (CNN) and the F1 score as metrics in a deep learning project for picture categorization which can localize license plates and recognize the letters. We work on license plate recognition and segments characters in the license plate firstly, and then recognizes each segmented character using Optical Character Recognition(OCR)techniques. Extensive experiments show the effectiveness and the efficiency of our proposed approach.

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M C, Sohan, Akanksh A M, Anala M R e Hemavathy R. "Banknote Denomination Recognition on Mobile Devices". ECS Transactions 107, n. 1 (24 aprile 2022): 11781–90. http://dx.doi.org/10.1149/10701.11781ecst.

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Abstract (sommario):

Several mobile applications have been developed to facilitate denomination detection for blind users. However, none of the existing applications allow for detecting multiple notes in a single frame and relaying the total denomination, nor is there a dataset available for the new Indian currency notes, annotated for object detection training. We describe the development of a detection application that aims to improve on the previously existing solutions by enabling multi-note detection, continuous audio feedback, automatic torch usage, and minimal user-application interaction. YOLOv4 allowed the training of a lightweight and fast object detection model with high accuracy on a custom-created dataset post-demonetization of Indian currencies that is deployed on a mobile device.

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G., Nirmala Priya. "Comparison of Partially Occluded Face Detection and Recognition Methods". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 luglio 2020): 201–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202099.

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C P, Anju, Andria Joy, Haritha Ashok, Joseph Ronald Pious e Livya George. "Traffic Sign Detection and Recognition". International Journal of Innovative Science and Research Technology 5, n. 7 (10 agosto 2020): 1143–46. http://dx.doi.org/10.38124/ijisrt20jul787.

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Abstract (sommario):

As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The proposed system not only tackles the above-mentioned problem, but also to an extent ensuressafer driving by reducing accidents through conveying the traffic signs properly. The techniques usedto implement the system include digital image processing, natural language processing and machinelearning concepts. The implementation of the system includesthree major steps which are detection of traffic sign from a captured traffic scene, classification of traffic signs and finally conversion of classified traffic signs to audio message.

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Katkar, Aniruddha. "EYE DISEASE RECOGNITION SYSTEM". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n. 04 (28 aprile 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32078.

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This paper presents an innovative system for detecting eye diseases utilizing advanced machine learning techniques. Given the increasing prevalence of eye disorders, early detection and intervention are of utmost importance. The proposed system integrates a diverse dataset comprising medical images and patient information. Deep learning algorithms are employed to extract intricate features from the dataset. These features are then input into a predictive model, facilitating accurate identification of potential eye diseases. Rigorous testing and validation demonstrate the system's performance and its ability to provide reliable predictions. The early diagnosis enabled by this system has the potential to significantly impact patient outcomes and contribute to the advancement of ophthalmic healthcare. The Eye Disease Detection System serves as a valuable tool for the early detection and management of various eye conditions. Through the integration of advanced technologies such as machine learning and medical imaging, this system enhances the accuracy and efficiency of the diagnostic process. Index Terms : Vision disorders, Glaucoma, Macular degeneration, Eye diseases, Ophthalmology, Corneal diseases.

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Yu, Myoungseok, Narae Kim, Yunho Jung e Seongjoo Lee. "A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar". Sensors 20, n. 8 (18 aprile 2020): 2321. http://dx.doi.org/10.3390/s20082321.

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In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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Adeyemi, Oladimeji, Martins Irhebhude e Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques". Advances in Image and Video Processing 7, n. 5 (8 novembre 2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.

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Abstract (sommario):

This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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Kamble, Kunal, Pranit Jadhav, Atharva Shanware e Pallavi Chitte. "Smart Surveillance System for Anomaly Recognition". ITM Web of Conferences 44 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224402003.

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Abstract (sommario):

Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and authority without any human interference. It is a very critical issue in a smart surveillance system to instantly detect an anomalous behaviour in video surveillance system. In this project, a unified framework based on deep neural network framework is proposed to detect anomalous activities. This neural network framework consists of (a) an object detection module, (b) an object discriminator and tracking module, (c) an anomalous activity detection module based on recurrent neural network. The system is a web application where user can apply for three different security services namely motion detection, fall detection and anomaly detection which is applicable for monitoring different environment like homes, roads, offices, schools, shops, etc. On detection of anomalous activity the system will notify the user and responsible authority regarding the anomaly through mail with an anomaly detected frame attachment.

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M. P, Milan. "CHALLENGES IN FACE RECOGNITION TECHNIQUE". Journal of University of Shanghai for Science and Technology 23, n. 07 (24 luglio 2021): 1201–4. http://dx.doi.org/10.51201/jusst/21/07253.

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Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial expressions, age variations…etc. The detection rate of face recognition algorithms is actually low in these conditions. With the popularity of AI in recent years, a mass number of enterprises deployed AI algorithms in absolute life settings. it is complete that face patterns observed by robots depend generally on variations such as pose, light environment, location.

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Chen, Yansong. "Face Mask Recognition based on YOLOV3". Highlights in Science, Engineering and Technology 85 (13 marzo 2024): 1214–22. http://dx.doi.org/10.54097/cgaz8m04.

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Wearing masks is considered one of the effective ways to prevent epidemics and protect physical health. In densely populated public places, detecting whether the mask is correctly worn has important application value, which can help managers reduce the potential risk of disease transmission. Previous works mainly rely on manual features, which are limited to fully expressing complex scene information. Thanks to the rapid development of artificial intelligence technology, automatic mask detection methods based on deep learning have achieved breakthroughs in speed and accuracy, while mask detection in complex scenes is still an open issue. To alleviate this issue, this paper proposes a mask detection method based on YOLOV3. Specifically, this study introduces regularization methods such as Drop Block and Label Smoothing, as well as data augmentation strategies such as Gridmask and Mosaic. This study further explores the recognition performance for refined scenarios including no mask, standard mask-wearing and incorrect mask-wearing. Extensive experimental results show the effectiveness of our method. By introducing the improved YOLOV5 algorithm to realize the mask detection function, the data structure is optimized through pruning, the data volume is reduced, the calculation speed is accelerated, and the mask detection is well realized.

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K., Santhosh Kumar. "Finger Vein Recognition Using Pattern Matching and Corner Detection Strategies". International Journal of Psychosocial Rehabilitation 24, n. 3 (30 marzo 2020): 2719–33. http://dx.doi.org/10.37200/ijpr/v24i3/pr2020308.

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Saleh, Kawther Thabt, Safa Ameen Ahmed e Haitham Salman Chyad. "Building Smart House based on Speech Detection and Recognition System". Webology 19, n. 1 (20 gennaio 2022): 5083–98. http://dx.doi.org/10.14704/web/v19i1/web19342.

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Abstract (sommario):

Technology in all areas has entered into life in homes (achievement of housework), streets and work, in order to facilitate life matters and saving effort and time, also, the technology has entered the areas of entertainment and communication and achievement of works even remotely and achievement of work that needs strength and short time. Also, artificial intelligence has entered various applications to improve its performance; this research will offer one of the smart technology applications, which is the smart home where the instructions can be handled and performed through speech, in order to add more facilities to our lives and help some people (e.g., elderly or disabled people who live alone) who have difficulty to move and handle the regular appliances in the house. And due to the lack of voice command recognition research in Arabic has resulted in making It difficult to stratify smart house voice command services, especially in the Middle East. Speech recognition is also a nontrivial task in the processing of natural language; this proposed smart house system is based upon detection and recognition of the speech in the Arabic language. Arduino programming was used to accomplish this system.

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Raut, Prathamesh. "Depression Detection using BDI, Speech Recognition and Facial Recognition". International Journal for Research in Applied Science and Engineering Technology 6, n. 4 (30 aprile 2018): 347–51. http://dx.doi.org/10.22214/ijraset.2018.4062.

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Günlü, Göksel. "Embedded Face Detection and Recognition". International Journal of Advanced Robotic Systems 9, n. 4 (gennaio 2012): 96. http://dx.doi.org/10.5772/51132.

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Gurnani, Divya, Vijay Gaikwad e Pradip V. "Intruder Detection and Recognition System". International Journal of Computer Applications 178, n. 20 (18 giugno 2019): 30–34. http://dx.doi.org/10.5120/ijca2019919030.

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Reddy, K. Manideep. "Face Recognition for Criminal Detection". International Journal for Research in Applied Science and Engineering Technology 10, n. 6 (30 giugno 2022): 2856–60. http://dx.doi.org/10.22214/ijraset.2022.44528.

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Abstract: In these days, assessment camera structure wins as a security system at high speed since this structure can screen from remote spots using Web camera joined to video screen by network. Besides, computerized supplies like Web camera, and hard circle drive are proficiently fabricated, and are sold for minimal price. Likewise, execution gain of these mechanized sorts of stuff improves at a fast rate. Current perception camera structure shows dynamic pictures from some oversight areas shot by various Web cameras all the while. Then, this system makes spectator's mind and body tired considering the way that he/she wants to watch enormous number of dynamic pictures been persistently strengthened. Moreover, this structure has a troublesome issue, which is an observer slips over mark of bad behavior. This study eliminates Motion Region from moving individual, and measures Motion Quantity for assessing his/her dynamic state. Also, this recommendation method finds the distinctive place of questionable activity, and checks the degree of risk of the questionable development.

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Pandey, Amit, Aman Gupta e Radhey Shyam. "FACIAL EMOTION DETECTION AND RECOGNITION". International Journal of Engineering Applied Sciences and Technology 7, n. 1 (1 maggio 2022): 176–79. http://dx.doi.org/10.33564/ijeast.2022.v07i01.027.

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Facial emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To maintain the texture picture's edge structure information, the retrieved edge information is placed on each feature image. In this research, several datasets are investigated and explored for training expression recognition models. The purpose of this paper is to make a study on face emotion detection and recognition via Machine learning algorithms and deep learning. This research work will present deeper insights into Face emotion detection and Recognition. It will also highlight the variables that have an impact on its efficacy.

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Manira*, Nishika, Swelia Monteiro, Tashya Alberto, Tracy Niasso e Supriya Patil. "Geo-Landmark Recognition and Detection". Regular issue 10, n. 7 (30 maggio 2021): 95–98. http://dx.doi.org/10.35940/ijitee.g8983.0510721.

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Abstract (sommario):

The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural Networks (CNN) based efficient recognition system that serves in navigation, to organize photo collections, identify fake reports and unlabeled landmarks from historical data. It identifies landmarks correctly from a variety of images taken at different viewpoints as well as distances. An appropriate CNN architecture helps to provide the best solution for the currently selected dataset.

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Chobhe, Snehal. "Criminal Detection through Face Recognition". International Journal for Research in Applied Science and Engineering Technology 9, n. 8 (31 agosto 2021): 1404–9. http://dx.doi.org/10.22214/ijraset.2021.37067.

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Abstract: Face acknowledge is a champion among the most troublesome subjects in PC vision today. It has applications running from security and perception to delight destinations. Face affirmation writing computer programs are significant in banks, plane terminals, and various associations for screening customers.Germany and Australia have passed on face affirmation at edges and customs for Automatic Passport Control. Human face is a unique dissent having significant degree of change in its appearance which makes stand up to affirmation an irksome issue in PC vision. In this field, exactness and speed of ID is a guidelineissue. Various challenges exist for defy affirmation. The force of the system can be hindered by individuals who change their facial features through wearing shaded contact central focuses, growing a mustache, putting on genuine make-up, etc. Moral concerns are also related to the way toward recording, mulling over, and seeingcountenances. Various individuals dont support of perceptionstructures which take different photographs of people who have not endorsed this action. The goal of this paper is to surveydefy disclosure and affirmation methodology and offer an allout response for picture based face area and affirmation with higher precision, better response rate and a basic development for video perception. Game plan is proposed considering performed tests on various face rich data sets similar to subjects, position, sentiments and light. Index Terms: Face acknowledge, Security and perception, Automatic Passport Control,Malware detection, facial features, face rich data sets.

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Chaudhari, V. J. "Face Recognition and Emotion Detection". International Journal for Research in Applied Science and Engineering Technology 9, n. VI (30 giugno 2021): 4775–77. http://dx.doi.org/10.22214/ijraset.2021.35698.

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Abstract (sommario):

This Face recognition and facial emotion detection is new era of technology. It’s also indirectly defining the level of growth in intelligence, security and copying human emotional behaviour. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural network or with inbuilt libraries like deepface. The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them the product which helps in determining their interest in it. Facial expression recognition technology can also be used in video game testing. During the video game testing, certain users are asked to play the game for a specified period and their expressions, and their behavior are monitored and analyzed. The game developers usually use the facial expression recognition and get the required insights and draw the conclusions and provide their feedback in the making of the final product. In this project, deep learning with the convolutional neural networks (CNN) approach is used. Neural networks need to be trained with large amounts of data and have a higher computational power [8-11]. It takes more time to train the model.[1]

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Gorban, Igor I., Vitalij N. Magera e Sergey V. Levij. "Crime‐detection speaker recognition system". Journal of the Acoustical Society of America 108, n. 5 (novembre 2000): 2575. http://dx.doi.org/10.1121/1.4743571.

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STRINGA, LUIGI. "EYES DETECTION FOR FACE RECOGNITION". Applied Artificial Intelligence 7, n. 4 (ottobre 1993): 365–82. http://dx.doi.org/10.1080/08839519308949995.

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Ye, Li Hua, e Hai Ming Yin. "Video-Tag Detection and Recognition". Applied Mechanics and Materials 58-60 (giugno 2011): 2528–33. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2528.

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In this work, we present a video-tag detection and recognition method. According to the duration of the video, choose an appropriate strategy to sample the frames. After the candidate tag of every frame is computed, a median filter algorithm is employed to get the tag boundary. At last the binary video-tag is determined by a multi-frame-based analysis algorithm. After scaling the binary tag image to the standard size, a full image-matching algorithm is used to recognize the tag. The experimental results indicate the proposed video-tag detection method has high recall ratio and precision ratio, and the image-matching-based video-tag recognition method performs much better than the traditional OCR methods.

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Li, Zhuan, Junling Wang, Chunmei Liu e Yonghan Xu. "Bucket detection for object recognition". Journal of Physics: Conference Series 1176 (marzo 2019): 042032. http://dx.doi.org/10.1088/1742-6596/1176/4/042032.

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Franco, Annalisa, Davide Maltoni e Serena Papi. "Grocery product detection and recognition". Expert Systems with Applications 81 (settembre 2017): 163–76. http://dx.doi.org/10.1016/j.eswa.2017.02.050.

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Boutin, Mireille. "Polygon Recognition and Symmetry Detection". Foundations of Computational Mathematics 3, n. 3 (1 agosto 2003): 227–71. http://dx.doi.org/10.1007/s10208-001-0027-5.

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Marcy, Theodore W. "Barotrauma: Detection, Recognition, and Management". Chest 104, n. 2 (agosto 1993): 578–84. http://dx.doi.org/10.1378/chest.104.2.578.

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S., S., Vibu Krishnan S., Mathan Raj Kumar, Ashok .. e M. Janakiraman. "Object Detection Using Deep Learning". Journal of Cognitive Human-Computer Interaction 6, n. 1 (2023): 32–38. http://dx.doi.org/10.54216/jchci.060103.

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Abstract (sommario):

Object recognition is an important task in computer vision that involves identifying the objects such as digital images or videos. This research paper provides a comprehensive review of the different techniques and applications of object recognition. The paper first discusses the basic concepts of object recognition, including feature extraction and matching, classification, and detection. Next, the paper reviews the different techniques for object recognition, such as template matching, PCA-based recognition, and deep learning-based recognition. The paper then presents an overview of the different applications of object recognition, including image and video classification, object tracking, face recognition, and autonomous driving. Finally, the paper ends with a discussion of the difficulties and likely new paths for object recognition.

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Uma, S., S. Prateeksha e V. Padmapriya. "A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition". Indian Journal Of Science And Technology 17, n. 12 (20 marzo 2024): 1203–12. http://dx.doi.org/10.17485/ijst/v17i12.2985.

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Abstract (sommario):

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

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Chen, Renxiang, e Xia Tian. "Gesture Detection and Recognition Based on Object Detection in Complex Background". Applied Sciences 13, n. 7 (31 marzo 2023): 4480. http://dx.doi.org/10.3390/app13074480.

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Abstract (sommario):

In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interference in complex background gesture images and enhancing the network’s robustness against complex backgrounds. Experimental verification was conducted on two complex background gesture datasets, EgoHands and TinyHGR, with recognition accuracies of mAP0.5:0.95 at 75.6% and 66.8%, respectively, and a recognition speed of 64 FPS for 640 × 640 input images. The results show that the proposed method can recognize gestures quickly and accurately with complex backgrounds, and has higher recognition accuracy and stronger robustness compared to YOLOv5l, YOLOv7, and other comparative algorithms.

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Kartashov, V. M., V. N. Oleynikov, S. A. Sheyko, S. I. Babkin, I. V. Koryttsev e O. V. Zubkov. "Peculiarities of small unmanned aerial vehicles detection and recognition". Radiotekhnika, n. 195 (28 dicembre 2018): 235–43. http://dx.doi.org/10.30837/rt.2018.4.195.24.

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Review and analysis of methods for detection and recognition of unmanned aerial vehicles (UAVs) are conducted. The channels for the detection of UAVs - acoustic, optical, radar, infrared, radio channel are considered. The advantages and disadvantages of the channels used are compared and appreciated. In the case of small UAVs, there are a number of significant difficulties and limitations. One of the directions in the UAVs detection is acoustic observation. The noise generated by the UAV propulsion system and the air propeller is a significant demasking feature. Creating and improving methods for detecting, guiding and recognizing small UAVs by the reception and processing their sound signals is an urgent task. When using such a method of detecting UAVs, frequency spectra, spectrograms, normalized autocorrelation functions, and phase portraits of acoustic signals are used. Estimates of spectral coefficients, determined by a discrete realization containing a predetermined number of samples, as well as parameters of autoregression models can serve as information signs of the UAVs sound image.

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Zheng, Xibin, Sinan Fang, Haitao Chen, Liang Peng e Zhi Ye. "Internal Detection of Ground-Penetrating Radar Images Using YOLOX-s with Modified Backbone". Electronics 12, n. 16 (20 agosto 2023): 3520. http://dx.doi.org/10.3390/electronics12163520.

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Geological radar is an important method used for detecting internal defects in tunnels. Automatic interpretation techniques can effectively reduce the subjectivity of manual identification, improve recognition accuracy, and increase detection efficiency. This paper proposes an automatic recognition approach for geological radar images (GPR) based on YOLOX-s, aimed at accurately detecting defects and steel arches in any direction. The method utilizes the YOLOX-s neural network and improves the backbone with Swin Transformer to enhance the recognition capability for small targets in geological radar images. To address irregular voids commonly observed in radar images, the CBAM attention mechanism is incorporated to improve the accuracy of detection annotations. We construct a dataset using field detection data that includes targets of different sizes and orientations, representing “voids” and “steel arches”. Our model tackles the challenges of traditional GPR image interpretation and enhances the automatic recognition accuracy and efficiency of radar image detection. In comparative experiments, our improved model achieves a recognition accuracy of 92% for voids and 94% for steel arches, as evaluated on the constructed dataset. Compared to YOLOX-s, the average precision is improved by 6.51%. These results indicate the superiority of our model in geological radar image interpretation.

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Intan, Indo, Nurdin Nurdin e Fitriaty Pangerang. "Facial recognition using multi edge detection and distance measure". IAES International Journal of Artificial Intelligence (IJ-AI) 12, n. 3 (1 settembre 2023): 1330. http://dx.doi.org/10.11591/ijai.v12.i3.pp1330-1342.

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Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.

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Biswas, Rubel, Jia Uddin e Md Junayed Hasan. "A New Approach of Iris Detection and Recognition". International Journal of Electrical and Computer Engineering (IJECE) 7, n. 5 (1 ottobre 2017): 2530. http://dx.doi.org/10.11591/ijece.v7i5.pp2530-2536.

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This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.

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Bashir, Sulaimon Adebayo, Andrei Petrovski e Daniel Doolan. "A framework for unsupervised change detection in activity recognition". International Journal of Pervasive Computing and Communications 13, n. 2 (5 giugno 2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.

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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance. Design/methodology/approach The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

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Cahyadi, Septian, Febri Damatraseta e Lodryck Lodefikus S. "Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition". Jurnal Informatika Kesatuan 1, n. 1 (13 luglio 2021): 81–90. http://dx.doi.org/10.37641/jikes.v1i1.775.

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Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application

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Kim, Juyoung, Beomseong Kim e Heesung Lee. "Fall Recognition Based on Time-Level Decision Fusion Classification". Applied Sciences 14, n. 2 (14 gennaio 2024): 709. http://dx.doi.org/10.3390/app14020709.

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We propose a vision-based fall detection algorithm using advanced deep learning models and fusion methods for smart safety management systems. By detecting falls through visual cues, it is possible to leverage existing surveillance cameras, thus minimizing the need for extensive additional equipment. Consequently, we developed a cost-effective fall detection system. The proposed system consists of four modules: object detection, pose estimation, action recognition, and result fusion. Constructing the fall detection system involved the utilization of state-of-the-art (SOTA) models. In the fusion module, we experimented with various approaches, including voting, maximum, averaging, and probabilistic fusion. Notably, we observed a significant performance improvement with the use of probabilistic fusion. We employed the HAR-UP dataset to demonstrate this enhancement, achieving an average 0.84% increase in accuracy compared to the baseline, which did not incorporate fusion methods. By applying our proposed time-level ensemble and skeleton-based fall detection approach, coupled with the use of enhanced object detection and pose estimation modules, we substantially improved the robustness and accuracy of the system, particularly for fall detection in challenging scenarios.

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Gao, Jingqian, Minqiang Xu, Huan Wang e Ji Zhou. "End-to-end Saliency Face Detection and Recognition". Journal of Physics: Conference Series 2171, n. 1 (1 gennaio 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2171/1/012004.

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Abstract Face recognition is a long-lasting hot topic in compute vision. The face recognition system mainly includes face detection, alignment and feature extraction. In the forward task, the extracted features are used to measure the similarity between faces, and outputs whether those are same person or not or which person it is in the registered set. Typically, the three stages of recognition system training independently of each other have the following shortcomings: 1) redundant calculation of feature maps; 2) unable to end-to-end optimization; 3) detecting an extracting so much useless face. A lightweight model for saliency face detection and recognition that can be optimized end-to-end is proposed. While maintaining accuracy, it meets the real-time and memory limitation requirements in embedded devices or terminals.

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Rao K Mahalakshmi, Nitalaksheswara. "A Novel Face Detection and Recognition System Using Machine Learning Approaches". International Journal of Science and Research (IJSR) 12, n. 6 (5 giugno 2023): 2730–38. http://dx.doi.org/10.21275/sr23626104114.

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Borkar, Yasar, Reeve Mascarenhas, Shubham Tambadkar e Jayanand P. Gawande. "Comparison of Real-Time Face Detection and Recognition Algorithms". ITM Web of Conferences 44 (2022): 03046. http://dx.doi.org/10.1051/itmconf/20224403046.

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With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.

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Zhi-Rong Zhong, Zhi-Rong Zhong, Hong-Fu Zuo Zhi-Rong Zhong, Shi-Ying Lei Hong-Fu Zuo, Jia-Chen Guo Shi-Ying Lei e Heng Jiang Jia-Chen Guo. "Semi-supervised Learning Based EEG Detection Approach for Rehabilitation Engineering". 電腦學刊 33, n. 3 (giugno 2022): 099–111. http://dx.doi.org/10.53106/199115992022063303008.

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<p>A semi-supervised learning based EEG signal detection method was studied in this paper. The feature engineering system of this paper was established, which contains novel AutoEncoders mapping features. The optimal channel combination for all subjects was determined to improve recognition accuracy by ReliefF algorithm and recursive feature elimination. What’s more, the semi-supervised learning method based on pseudo-labelling was introduced to the character recognition method, in which the training samples were dynamically reorganized and updated, so that the proposed method could complete the symbol recognition with limited number of training samples. Based on the features extacted and the optimal channel combination, the recognition accuracy of the character recognition method can reach up to 100%.</p> <p> </p>

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Ahire, Pritam, Saiprasad Kadam e Ajay Jagtap. "Image Enhancement and Automated Number Plate Recognition". International Journal of Science and Healthcare Research 8, n. 2 (28 aprile 2023): 178–86. http://dx.doi.org/10.52403/ijshr.20230221.

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Automatic Number Plate Recognition (ANPR) and Vehicle Number plate detection (VNPD) systems. ANPR, is a technology that enables automatic detection, recognition, and identification of vehicle license plates, while VNPD is a subset of ANPR that focuses specifically on detecting and recognizing license plates.Several researchers have explored different approaches to ANPR and VNPD systems, as evidenced by the various papers listed. For instance, presented an e security system for vehicles number tracking at a parking lot, proposed a method of monitoring traffic signals, and violations using ANPR and GSM. In order to proceed, the conventional Grab Cut algorithm must first interactively give a candidate frame. for the target detection job to be done.To automate the identification of the licence plate by the Grab Cut algorithm, we update the candidate frame by incorporating the aspect ratio of the licence plate as the foreground extraction feature. Then, to fully implement picture noise reduction, we combined the Bernsen algorithm with the Wiener filter, which is extensively used in the fields of digital signal processing in order to increase the detection precision of conventional target identification techniques. Overall, the papers listed in the question demonstrate the wide-ranging applications of ANPR and VNPD technologies, from parking lot security to traffic signal control to driver assistance systems. These technologies have the potential to improve safety, efficiency, and security on the road, and researchers continue to explore new approaches to their development and implementation. Keywords: Detection of Number Plate, Convolutional Neural Network-(CNN), Object detection, character identification, Machine Learning-(ML).

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Ruiz, Idoia, e Joan Serrat. "Hierarchical Novelty Detection for Traffic Sign Recognition". Sensors 22, n. 12 (10 giugno 2022): 4389. http://dx.doi.org/10.3390/s22124389.

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Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.

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Wakchaure, Shraddha, Avanti Tambe, Pratik Gadhave, Shubham Sandanshiv e Mrs Archana Kadam. "Smart Exam Proctoring System". International Journal for Research in Applied Science and Engineering Technology 11, n. 4 (30 aprile 2023): 4507–10. http://dx.doi.org/10.22214/ijraset.2023.51358.

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Abstract: As the world is shifting towards digitalization, mostof the exams and assessments are being conducted online. These exams must be proctored. Several students are accessing thetest at the same time. It is very difficult to manually look if a student is committing malpractice. This project aims to use face detection and recognition for proctoring exams. Face detectionis the process of detecting faces in a video or image while face recognition is identifying or verifying a face from images orvideos. There are several research studies done on the detectionand recognition of faces owing to the requirement for securityfor economic transactions, authorization, national safety andsecurity, and other important factors. Exam proctoring platformsshould be capable of detecting cheating and malpractices like face is not on the screen, gaze estimation, mobile phone detection,multiple face detection, etc. This project uses face identificationusing HAAR Cascades Algorithm and face recognition using theLocal Binary Pattern Histogram algorithm. This system can beused in the future in corporate offices, schools, and universities.

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Wu, Q., E. Liu, Y. H. He e X. Tang. "Application Research on Extreme Learning Machine in Rapid Detection of Tool Wear in Machine Tools". Journal of Physics: Conference Series 2025, n. 1 (1 settembre 2021): 012091. http://dx.doi.org/10.1088/1742-6596/2025/1/012091.

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Abstract In order to put an end to the product quality accidents caused by cutting tool breakage or severe wear in machining process, the paper explored an ELM model detection method based on voice recognition. In this paper, firstly it analysed the features of cutting sound signal in time-frequency domain, then discussed the extraction method of tool working state sensitive spectral energy statistical feature based on wavelet packet decomposition, and finally constructed an ELM fast detection model based on sound feature recognition. The experimental results demonstrated that the ELM detection model could achieve higher detection accuracy and faster response time. The simulation results show that the ELM model is effective and applicable in detecting tool wear with the help of sound recognition.

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Zheng, Qihang, e Yaping Zhang. "Text Detection and Recognition for X-ray Weld Seam Images". Applied Sciences 14, n. 6 (13 marzo 2024): 2422. http://dx.doi.org/10.3390/app14062422.

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X-ray weld seam images carry vital information about welds. Leveraging graphic–text recognition technology enables intelligent data collection in complex industrial environments, promising significant improvements in work efficiency. This study focuses on using deep learning methods to enhance the accuracy and efficiency of detecting weld seam information. We began by actively gathering a dataset of X-ray weld seam images for model training and evaluation. The study comprises two main components: text detection and text recognition. For text detection, we employed a model based on the DBNet algorithm and tailored post-processing techniques to the unique features of weld seam images. Through model training, we achieved efficient detection of the text regions, with 91% precision, 92.4% recall, and a 91.7% F1 score on the test dataset. In the text recognition phase, we introduced modules like CA, CBAM, and HFA to capture the character position information and global text features effectively. This optimization led to a remarkable text line recognition accuracy of 93.4%. In conclusion, our study provides an efficient deep learning solution for text detection and recognition in X-ray weld seam images, offering robust support for weld seam information collection in industrial manufacturing.

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