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Journal articles on the topic 'Lines detection and segmentation'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Wang, Shengli, Zhangpeng Zhou, and Wenbin Zhao. "Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines." Journal of Physics: Conference Series 2185, no. 1 (2022): 012086. http://dx.doi.org/10.1088/1742-6596/2185/1/012086.

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Abstract Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can i

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Chen, Mo, Sheng Cheng, Yan Liu, Qifan Yin, and Hongfu Zuo. "A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines." Applied Sciences 15, no. 10 (2025): 5342. https://doi.org/10.3390/app15105342.

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Checking the distance between aircraft fire detection lines is a crucial task in the conformity inspection process of civil aircraft manufacturing. Currently, this task is mainly performed manually, which is inefficient and prone to errors and omissions. To address this issue, we propose a method for detecting the distance between aircraft fire detection lines based on the Segment Anything Model (SAM). In this method, we develop a general model for aircraft parts image segmentation and detection, named the Aircraft Segment Anything Model (ASAM). This model uses a low-rank fine-tuning strategy

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Tao, Zhen, Shiwei Ren, Yueting Shi, Xiaohua Wang, and Weijiang Wang. "Accurate and Lightweight RailNet for Real-Time Rail Line Detection." Electronics 10, no. 16 (2021): 2038. http://dx.doi.org/10.3390/electronics10162038.

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Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, whi

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Li, Aohua, Dacheng Li, and Anjing Wang. "A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines." Sensors 25, no. 9 (2025): 2903. https://doi.org/10.3390/s25092903.

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Transmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then uses U-Net for the fine segmentation of candidate regions. To improve the segmentation performance, U-Net adopts a transfer learning strategy based on the VGG16 pretrained model to alleviate the impact of limited dataset size on the training effect. Meanwhile, a hybrid loss function that combines D

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Song, Xiang, Xiaoyu Che, Huilin Jiang, et al. "A Robust Detection Method for Multilane Lines in Complex Traffic Scenes." Mathematical Problems in Engineering 2022 (March 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/7919875.

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The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed in

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Abbasi, Soolmaz, Assefa Seyoum Wahd, Shrimanti Ghosh, et al. "Improved A-Line and B-Line Detection in Lung Ultrasound Using Deep Learning with Boundary-Aware Dice Loss." Bioengineering 12, no. 3 (2025): 311. https://doi.org/10.3390/bioengineering12030311.

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Lung ultrasound (LUS) is a non-invasive bedside imaging technique for diagnosing pulmonary conditions, especially in critical care settings. A-lines and B-lines are important features in LUS images that help to assess lung health and identify changes in lung tissue. However, accurately detecting and segmenting these lines remains challenging, due to their subtle blurred boundaries. To address this, we propose TransBound-UNet, a novel segmentation model that integrates a transformer-based encoder with boundary-aware Dice loss to enhance medical image segmentation. This loss function incorporate

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Yan, Jichen, Xiaoguang Zhang, Siyang Shen, et al. "A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs." Drones 7, no. 9 (2023): 574. http://dx.doi.org/10.3390/drones7090574.

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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which

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Xing, Junyao, Xiaojun Bi, and Yu Weng. "A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture." Mathematics 11, no. 15 (2023): 3392. http://dx.doi.org/10.3390/math11153392.

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Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise

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Chen, Yong, Yun-hui Wang, Song Li, and Meng Li. "Transmission Line Instance Segmentation Algorithm Based on YOLACT." Journal of Physics: Conference Series 2562, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1742-6596/2562/1/012018.

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Abstract In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101.

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Zhu, Yuhang, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai, and Hongchuan Zhang. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation." Sensors 24, no. 5 (2024): 1635. http://dx.doi.org/10.3390/s24051635.

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently e

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Lee, Jaehyun, Keunwoo Lee, Jaewon Yang, Young-Jin Kim, and Seung-Woo Kim. "Comb segmentation spectroscopy for rapid detection of molecular absorption lines." Optics Express 27, no. 6 (2019): 9088. http://dx.doi.org/10.1364/oe.27.009088.

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Kavallieratou, Ergina, and Fotis Daskas. "Text Line Detection and Segmentation: Uneven Skew Angles and Hill-and-Dale Writing." JUCS - Journal of Universal Computer Science 17, no. (1) (2011): 16–29. https://doi.org/10.3217/jucs-017-01-0016.

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In this paper a line detection and segmentation technique is presented. The proposed technique is an improved version of an older one. The experiments have been performed on the training dataset of the ICDAR 2009 handwriting segmentation contest in order to be able to compare, objectively, the performance of the two techniques. The improvement between the older and newer version is more than 24% while the average extra CPU time cost is less than 200 ms per page.

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Cheng, Wangfeng, Xuanyao Wang, and Bangguo Mao. "Research on Lane Line Detection Algorithm Based on Instance Segmentation." Sensors 23, no. 2 (2023): 789. http://dx.doi.org/10.3390/s23020789.

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Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which en

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Merzliakova, Marina A., Boris M. Shurygin, Alexei E. Solovchenko, Andrey S. Krylov, and Dmitry V. Sorokin. "Segmentation of Foreground Row Trees in Apple Orchard Images Collected by Ground Vehicles." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-2/W1-2024 (December 16, 2024): 25–30. https://doi.org/10.5194/isprs-annals-x-2-w1-2024-25-2024.

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Abstract. This paper proposes a fully automatic method for the segmentation of foreground row trees in industrial apple orchard images. The segmentation is based on analyzing a combination of a depth map constructed by the Marigold diffusion model and a model depth map created using automatically detected vanishing lines. The output of the method is a binary mask of the selected foreground trees. These masks can be used in subsequent stages of the image processing pipeline to discard false detections in the fruit counting module. The proposed method was evaluated as a preprocessing step for an

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Hu, Ding, Zihao Zheng, Yafei Liu, Chengkang Liu, and Xiaoguo Zhang. "Axial-UNet Power Line Detection Network Based on Gated Axial Attention Mechanism++." Remote Sensing 16, no. 23 (2024): 4585. https://doi.org/10.3390/rs16234585.

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The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extra

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Tang, Yang Shan, Dao Hua Xia, Gui Yang Zhang, Li Na Ge, and Xin Yang Yan. "The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation." Applied Mechanics and Materials 741 (March 2015): 354–58. http://dx.doi.org/10.4028/www.scientific.net/amm.741.354.

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For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentatio

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Zhu, Q., W. Jiang, and J. Zhang. "FEATURE LINE BASED BUILDING DETECTION AND RECONSTRUCTION FROM OBLIQUE AIRBORNE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 12, 2015): 199–204. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-199-2015.

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In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines a

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Wang, W., F. Berholm, K. Hu, et al. "Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix." Information Technology and Control 50, no. 4 (2021): 722–35. http://dx.doi.org/10.5755/j01.itc.50.4.29094.

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To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detect

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Triwijoyo, Bambang Krismono. "Segmentasi Citra Pembuluh Darah Retina Menggunakan Metode Deteksi Garis Multi Skala." Jurnal Matrik 15, no. 1 (2017): 13. http://dx.doi.org/10.30812/matrik.v15i1.28.

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Changes in retinal blood vessels feature a sign of serious illnesses such as heart disease and stroke. Therefore, the analysis of retinal vascular features can help in detecting these changes and allow patients to take preventive measures at an early stage of this disease. Automation of this process will help reduce the costs associated with the specialist and eliminate inconsistencies that occur in manual detection system. Among the retinal image analysis, image extraction retinal blood vessels is a crucial step before measurement. In this paper, we use an effective methodof automatically ext

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Yang, Ranbing, Yuming Zhai, Jian Zhang, et al. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation." Agriculture 12, no. 9 (2022): 1363. http://dx.doi.org/10.3390/agriculture12091363.

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Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The fi

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He, Lei, Shuang Wang, and Yongcun Guo. "Detection of Pits by Conjugate Lines: An Algorithm for Segmentation of Overlapping and Adhesion Targets in DE-XRT Sorting Images of Coal and Gangue." Applied Sciences 12, no. 19 (2022): 9850. http://dx.doi.org/10.3390/app12199850.

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In lump coal and gangue separation based on photoelectric technology, the prerequisite of using a dual-energy X-ray to locate and identify coal and gangue is to obtain the independent target area. However, with the increase in the input of the sorting system, the actual collected images had adhesion and overlapping targets. This paper proposes a pit point detection and segmentation algorithm to solve the problem of overlapping and adhesion targets. The adhesion forms are divided into open and closed-loop adhesion (OLA and CLA). Then, an open- and closed-loop crossing algorithm (OLCA and CLCA)

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Malik, Saud, Ahthasham Sajid, Arshad Ahmad, et al. "An Efficient Skewed Line Segmentation Technique for Cursive Script OCR." Scientific Programming 2020 (December 3, 2020): 1–12. http://dx.doi.org/10.1155/2020/8866041.

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Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the syste

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Sindel, Aline, Thomas Klinke, Andreas Maier, and Vincent Christlein. "ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints." Journal of Imaging 7, no. 7 (2021): 120. http://dx.doi.org/10.3390/jimaging7070120.

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The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable

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Liu, Huaming, Rumeng Shi, Xuehui Bi, Xiuyou Wang, and Weilan Wang. "Line Segmentation of Tibetan Ancient Books Based on A* Algorithm." Journal of Physics: Conference Series 2356, no. 1 (2022): 012046. http://dx.doi.org/10.1088/1742-6596/2356/1/012046.

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Line segmentation is an important step in image character recognition. However, due to the problems of interline adhesion, overlapping, and skewing of document images, the effect of text line segmentation is not ideal. Therefore, further research on image line segmentation of Tibetan ancient books is urgently needed. The standard A* algorithm is not ideal for line segmentation of Tibetan ancient books, so this paper proposes a block-based A* algorithm for line segmentation of Tibetan ancient books. The method firstly preprocesses the image such as binarization and tilt correction and then perf

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Muhammad Naufal Mansor, Mohd Zamri Hasan, Wan Azani Mustafa, et al. "Leukemia Blood Cells Detection using Neural Network Classifier." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 152–62. http://dx.doi.org/10.37934/araset.33.1.152162.

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Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necess

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Zhu, Sha, Lu Li, Jianwei Zhao, Chunguang Zhang, Shaofeng Ni, and Yiping Chen. "Tower and power line segmentation method based on RandLA-Net." Journal of Physics: Conference Series 2863, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2863/1/012017.

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Abstract The scale of the power grid of China has gradually expanded, and the number of long-distance and high-level transmission lines has also increased. However, it is very hard to detect the power infrastructure transmission lines because of the complex laying environment and various ground features. Lines and towers in the power system are important components of power transmission, and their safety directly affects the operation of the entire power system. Reasonable management of lines and towers can reduce the occurrence of faults and system safety risks. At present, both manual inspec

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Chen, He, Nan Li, Tian Chen Huang, and Rong Xia Duan. "Research on TV Goniometer Object Extraction Algorithm Based on Threshold Segmentation." Advanced Materials Research 889-890 (February 2014): 1093–98. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1093.

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In the TV goniometer detection system, to play the signal and field of view points line extraction is a key link in the process of parameter detection. Combination of target processing requirements, this article will target extraction algorithm based on gray level threshold and edge detection algorithm is studied, and through the experimental analysis to select the optimal algorithm was applied to the detection of TV goniometer; According to the characteristics of the standard signal and view points, lines, and put forward the corresponding methods of target recognition, and is verified throug

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P V, Pearlsy, and Deepa Sankar. "Handwriting-Based Text Line Segmentation from Malayalam Documents." Applied Sciences 13, no. 17 (2023): 9712. http://dx.doi.org/10.3390/app13179712.

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Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical pr

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HIREMATH, P. S., and AJIT DANTI. "DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 01 (2006): 39–61. http://dx.doi.org/10.1142/s021800140600451x.

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In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented region

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Li, Guoxu, Feixiang Le, Shuning Si, Longfei Cui, and Xinyu Xue. "Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot." Agronomy 14, no. 9 (2024): 1886. http://dx.doi.org/10.3390/agronomy14091886.

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The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional

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Vrochidou, Eleni, George K. Sidiropoulos, Athanasios G. Ouzounis, et al. "Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning." Electronics 11, no. 20 (2022): 3289. http://dx.doi.org/10.3390/electronics11203289.

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Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segm

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Droby, Ahmad, Berat Kurar Barakat, Reem Alaasam, Boraq Madi, Irina Rabaev, and Jihad El-Sana. "Text Line Extraction in Historical Documents Using Mask R-CNN." Signals 3, no. 3 (2022): 535–49. http://dx.doi.org/10.3390/signals3030032.

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Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text

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Bojarczak, Piotr, Piotr Lesiak, and Waldemar Nowakowski. "Automatic Detection of Ballast Unevenness Using Deep Neural Network." Applied Sciences 14, no. 7 (2024): 2811. http://dx.doi.org/10.3390/app14072811.

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The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For

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Ait El Attar, Hicham, Hassan Samri, Moulay El Houssine Ech-Chhibat, Khalifa Mansouri, Abderrahim Bahani, and Tarek Bahrar. "U-Net for wheel rim contour detection in robotic deburring." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1363. https://doi.org/10.11591/ijai.v14.i2.pp1363-1376.

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Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wh

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Hicham, Ait El Attar, Samri Hassan, El Houssine Ech-Chhibat Moulay, Mansouri Khalifa, Bahani Abderrahim, and Bahrar Tarek. "U-Net for wheel rim contour detection in robotic deburring." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1363–76. https://doi.org/10.11591/ijai.v14.i2.pp1363-1376.

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Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wh

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Liu, Zhan Wen, Shan Lin, and Sheng Gen Dou. "A Novel Video Detection System on Traffic Flow Inspection." Applied Mechanics and Materials 182-183 (June 2012): 440–44. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.440.

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A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomp

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Lei, Wenyang, Fang Yuan, Jiang Guo, et al. "Vision-Based Real-Time Bolt Loosening Detection by Identifying Anti-Loosening Lines." Sensors 24, no. 20 (2024): 6747. http://dx.doi.org/10.3390/s24206747.

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Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting tec

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Aya, Abdullateef Ezat Qusay Abboodi Ali Muhaned Al-Hashimi. "A Systematic Review of Vehicle License Plate Recognition Algorithms Based on Image Segmentation." LC International Journal of STEM (ISSN: 2708-7123) 4, no. 2 (2023): 25–34. https://doi.org/10.5281/zenodo.8239534.

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Recently, vehicle license plate recognition (VLPR) is a very significant topic in smart transportation. License plate (LP) has become an important and difficult research problem in recent years due to its difficulties such as detection speed, noise, effects, various lighting, and others. In the same context, most VLPR algorithms include should have many methods to be able to identify LP images based on different letters, colors, languages, complex backgrounds, distortions, hazardous situations, obstructions, vehicle speeds, vertical or horizontal lines, horizontal slopes, and lighting.  T

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Yumeng, Xie, Noridayu Binti Manshor, Nor Azura Husin, and Liu Chengzhi. "Improving YoloPX using YoloP and Yolov8 for Panoptic Driving Perception." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 248. https://doi.org/10.62527/joiv.9.1.3791.

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Autonomous driving technology (ADS) has seen significant advancements over the past decade, with car manufacturers investing heavily in its development to meet the growing demand for safer, more efficient, and eco-friendly transportation solutions. The panoptic driving perception system is central to ADS, essential for accurately interpreting the driving environment. This system requires high precision, lightweight design, and real-time responsiveness to detect surrounding vehicles, lane lines, and drivable areas effectively. This study introduces an enhanced YOLOPX model that combines YOLOP a

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Guo, Jiayi, Xuelin Guo, and Limin Wang. "The Detection Algorithm of Broken Wires in Power Lines Based on Grabcut Segmentation." IOP Conference Series: Materials Science and Engineering 768 (March 31, 2020): 072017. http://dx.doi.org/10.1088/1757-899x/768/7/072017.

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Yu, Hao, Zhengyang Wang, Qingjie Zhou, et al. "Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines." Remote Sensing 15, no. 9 (2023): 2371. http://dx.doi.org/10.3390/rs15092371.

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The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficien

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Zhang, Wenli, and Qingfeng Gao. "Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots." Academic Journal of Science and Technology 7, no. 1 (2023): 131–36. http://dx.doi.org/10.54097/ajst.v7i1.11299.

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To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while als

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Chen, Tianao, and Aotian Chen. "Road Sign Recognition Method Based on Segmentation and Attention Mechanism." Mobile Information Systems 2022 (June 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/6389580.

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With the development of autonomous driving, low-cost visual perception solutions have become a current research hotspot. However, the performance of the pure visual scheme in unfriendly environments such as low light, rain and fog, and complex traffic scenes has a large room for improvement. Moreover, with the development and application of deep learning, the balance between the accuracy and real-time performance of deep learning models is a difficult problem for current research. Aiming at the problems of large differences in the target scale of pavement signs and the difficulty of balancing

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Kim, Seongje, Van-Doi Truong, Kwang-Hee Lee, and Jonghun Yoon. "Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading." Sensors 24, no. 5 (2024): 1473. http://dx.doi.org/10.3390/s24051473.

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Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parce

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Ning, Shanping, Feng Ding, and Bangbang Chen. "Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning." Sensors 24, no. 14 (2024): 4483. http://dx.doi.org/10.3390/s24144483.

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Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row

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Savage, C. J., and D. H. Foster. "Target Detection and Texture Segmentation in Briefly Presented Displays of Curved-line Elements." Perception 25, no. 1_suppl (1996): 135. http://dx.doi.org/10.1068/v96l0504.

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Similar pre-attentive processes are often thought to underlie rapid texture segmentation and target ‘pop-out’ in multi-element displays (but see Wolfe, 1992 Vision Research32 757 – 763). Performance in target-detection and texture-segmentation tasks was measured here for briefly presented displays of curved-line elements. In both tasks 49 curved-line elements, each subtending 1 deg of visual angle, were presented in a circular display for 100 ms and followed by a mask. The position of each element in the array was jittered to reduce any possible collinearity or luminance cues. In the target-de

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Moussaoui, Hanae, Nabil El Akkad, and Mohamed Benslimane. "License plate text recognition using deep learning, NLP, and image processing techniques." Statistics, Optimization & Information Computing 12, no. 3 (2024): 685–96. http://dx.doi.org/10.19139/soic-2310-5070-1966.

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Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the det

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Kim, JongBae. "Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera." Symmetry 11, no. 12 (2019): 1492. http://dx.doi.org/10.3390/sym11121492.

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Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In

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Pötzi, Werner, Gernot Riegler, Astrid Veronig, Thomas Pock, and Ute Möstl. "A system for near real-time detection of filament eruptions at Kanzelhöhe Observatory." Proceedings of the International Astronomical Union 8, S300 (2013): 519–20. http://dx.doi.org/10.1017/s1743921313011800.

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AbstractKanzelhöhe Observatory (kso.ac.at) performs regular high-cadence full-disk observations of the solar chromosphere in the Hα and CaIIK spectral lines as well as the solar photosphere in white-light. In the frame of ESA's Space Situational Awareness (SSA) activities, a new system for near real-time Hα image provision through the SSA Space Weather (SWE) portal (swe.ssa.esa.int) and for automatic alerting of flares and erupting filaments is under development. Image segmentation algorithms, based on optical flow image registration, for the automatic detection of solar filaments in real time

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Mapayi, Temitope, Jules-Raymond Tapamo, Serestina Viriri, and Adedayo Adio. "AUTOMATIC RETINAL VESSEL DETECTION AND TORTUOSITY MEASUREMENT." Image Analysis & Stereology 35, no. 2 (2016): 117. http://dx.doi.org/10.5566/ias.1421.

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As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents study on the combination of difference image and K-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are us

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