CNN network classes require multiple images per class to train. This makes facial recognition using CNN imprac- tical, as it is often hard to obtain a sufficient number of images of one person. Siamese Networks, on the other hand, uses oneshot learning, meaning that only one input image will be needed to train the network for each person. We build a facial recognition system using Siamese Network. In Siamese Networks, a single image of one person is input, and the network will learn to recognize the person by learning the embedding of the image. The embedding is used to compute a similarity score – similar images will have higher similarity scores. Another image will then be input to the same network, and the system will compare two embeddings to determine whether they contain the same person, giving a true or false output. Using the ORL and LFW dataset, we performed several experiments on multiple aspects of the Siamese Network. We experimented on the Random Erasing function for our augmented data to test the reliability of the network in facial recognition. Results show significant improvement on model accuracy for model trained on random erasing masking. This kind of facial recognition systems is versatile and can be applied to numerous use cases. For example, this kind of system can be used to provide facial recognition for persons with disability that manifests in the deformation of facial features.
{"title":"One-shot deformed face recognition via Siamese neural network","authors":"Jay Zhu","doi":"10.1117/12.3014396","DOIUrl":"https://doi.org/10.1117/12.3014396","url":null,"abstract":"CNN network classes require multiple images per class to train. This makes facial recognition using CNN imprac- tical, as it is often hard to obtain a sufficient number of images of one person. Siamese Networks, on the other hand, uses oneshot learning, meaning that only one input image will be needed to train the network for each person. We build a facial recognition system using Siamese Network. In Siamese Networks, a single image of one person is input, and the network will learn to recognize the person by learning the embedding of the image. The embedding is used to compute a similarity score – similar images will have higher similarity scores. Another image will then be input to the same network, and the system will compare two embeddings to determine whether they contain the same person, giving a true or false output. Using the ORL and LFW dataset, we performed several experiments on multiple aspects of the Siamese Network. We experimented on the Random Erasing function for our augmented data to test the reliability of the network in facial recognition. Results show significant improvement on model accuracy for model trained on random erasing masking. This kind of facial recognition systems is versatile and can be applied to numerous use cases. For example, this kind of system can be used to provide facial recognition for persons with disability that manifests in the deformation of facial features.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"42 1","pages":"129692I - 129692I-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conventional road boundary intelligent identification methods mainly use black-and-white optical driving method to generate road boundary detection binary image, which is easily influenced by threshold segmentation, resulting in large deviation parameters of parabola identification. Therefore, it is necessary to design a brand-new road boundary intelligent identification method based on image segmentation and edge features. That is to say, the road boundary is extracted by using the image edge features, and an intelligent road boundary identification algorithm is designed in combination with image segmentation, thus completing the intelligent road boundary identification. The experimental results show that the intelligent road boundary recognition method based on image segmentation and edge features has good recognition effect, reliability and certain application value, and has made certain contributions to improving driving safety.
{"title":"Intelligent road boundary identification method based on image segmentation and edge features","authors":"Hong Li, Norriza Hussin","doi":"10.1117/12.3014460","DOIUrl":"https://doi.org/10.1117/12.3014460","url":null,"abstract":"Conventional road boundary intelligent identification methods mainly use black-and-white optical driving method to generate road boundary detection binary image, which is easily influenced by threshold segmentation, resulting in large deviation parameters of parabola identification. Therefore, it is necessary to design a brand-new road boundary intelligent identification method based on image segmentation and edge features. That is to say, the road boundary is extracted by using the image edge features, and an intelligent road boundary identification algorithm is designed in combination with image segmentation, thus completing the intelligent road boundary identification. The experimental results show that the intelligent road boundary recognition method based on image segmentation and edge features has good recognition effect, reliability and certain application value, and has made certain contributions to improving driving safety.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"12 5","pages":"129691B - 129691B-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper focuses on the feasibility study of vision-based deformation monitoring. Sparse optical flow, a computer vision technique, is often used for deformation tracking and extraction. The objective of this study is to explore the application of optical flow in concrete column deformation monitoring. The performance of the technique is compared with the traditional LVDT (Linear Variable Differential Transformer) measurement method. The experimental results show that the optical flow method can effectively extract the deformation information of concrete columns with a smaller error compared with the LVDT method, whose relative standard deviation is 1.03 mm and relative error is 1.88%, confirming the feasibility and effectiveness of the vision-based approach. This study provides a visual solution for deformation monitoring of concrete columns, and provides reference and guidance for deformation analysis and structural monitoring in related fields.
{"title":"A feasibility study of computer vision-based deformation monitoring for RC columns","authors":"Yankang Zhai, Alex Hay-Man Ng","doi":"10.1117/12.3014399","DOIUrl":"https://doi.org/10.1117/12.3014399","url":null,"abstract":"This paper focuses on the feasibility study of vision-based deformation monitoring. Sparse optical flow, a computer vision technique, is often used for deformation tracking and extraction. The objective of this study is to explore the application of optical flow in concrete column deformation monitoring. The performance of the technique is compared with the traditional LVDT (Linear Variable Differential Transformer) measurement method. The experimental results show that the optical flow method can effectively extract the deformation information of concrete columns with a smaller error compared with the LVDT method, whose relative standard deviation is 1.03 mm and relative error is 1.88%, confirming the feasibility and effectiveness of the vision-based approach. This study provides a visual solution for deformation monitoring of concrete columns, and provides reference and guidance for deformation analysis and structural monitoring in related fields.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"64 2","pages":"1296924 - 1296924-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel approach to accelerate Magnetic Resonance Imaging (MRI) using 1-dimensional undersampling and compressed sensing. By strategically applying under-sampling to rows through a Gaussian distribution, the proposed method aims to reduce the number of samples required for image reconstruction while maintaining image quality. The reconstruction process involves denoising with a Projection Over Convex Sets (POCS) algorithm, optimizing the threshold parameter lambda (λ) for effective denoising and convergence. Simulation results showcase the method’s effectiveness. Reconstructed images at varying under-sampling rates illustrate the gradual reduction of artifacts with increased mid-frequency sampling. The study also explores different lambda settings during reconstruction, highlighting the balance between denoising and convergence. While this approach shows promise for accelerating MRI and other imaging applications, challenges include evaluating alternative "mask" matrices and exploring under-sampling patterns beyond Gaussian distribution. The paper concludes by emphasizing compressed sensing’s potential to enhance applications constrained by scan time, fostering optimism for broader adoption.
{"title":"Rapid magnetic resonance imaging based on one dimensional under-sampling","authors":"Peiyao Sun, Qiyang Gu, Ruitong Wang","doi":"10.1117/12.3014564","DOIUrl":"https://doi.org/10.1117/12.3014564","url":null,"abstract":"This paper introduces a novel approach to accelerate Magnetic Resonance Imaging (MRI) using 1-dimensional undersampling and compressed sensing. By strategically applying under-sampling to rows through a Gaussian distribution, the proposed method aims to reduce the number of samples required for image reconstruction while maintaining image quality. The reconstruction process involves denoising with a Projection Over Convex Sets (POCS) algorithm, optimizing the threshold parameter lambda (λ) for effective denoising and convergence. Simulation results showcase the method’s effectiveness. Reconstructed images at varying under-sampling rates illustrate the gradual reduction of artifacts with increased mid-frequency sampling. The study also explores different lambda settings during reconstruction, highlighting the balance between denoising and convergence. While this approach shows promise for accelerating MRI and other imaging applications, challenges include evaluating alternative \"mask\" matrices and exploring under-sampling patterns beyond Gaussian distribution. The paper concludes by emphasizing compressed sensing’s potential to enhance applications constrained by scan time, fostering optimism for broader adoption.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"175 2","pages":"129691J - 129691J-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuxing Tong, Yaowei Xu, Qunshan Yan, Ben Liu, Xiangbin Tang, Song Gao, Ziwei Wang, Dejin Kong
In order to address the drawbacks of using offline and manual methods to measure the length of reconstituted cut tobacco using thick pulp method, an online automatic detection device is designed in the paper. A cut tobacco sampling and spreading out process is designed. Cut tobacco sampling is completed by using a robotic arm. Cut tobacco feeding is completed by simulating the manual shaking movement. A two-stage conveyor belt keeps spreading out tobacco leaves. A tobacco leaves image acquisition device have been designed to collect diluted cut tobacco. A software system has been developed, specifically for the case of a small amount of cross cut tobacco. A stitching algorithm based on sub pixel skeleton contour slope has been proposed to separate a single cut tobacco and facilitate tobacco length detection. The test results indicate that the system has the advantages of automation, online detection, and high measurement accuracy, which can meet the requirements of the production process of reconstituted tobacco leaves.
{"title":"Design and implementation of an online length detection device for reconstituted cut tobacco based on machine vision","authors":"Yuxing Tong, Yaowei Xu, Qunshan Yan, Ben Liu, Xiangbin Tang, Song Gao, Ziwei Wang, Dejin Kong","doi":"10.1117/12.3014610","DOIUrl":"https://doi.org/10.1117/12.3014610","url":null,"abstract":"In order to address the drawbacks of using offline and manual methods to measure the length of reconstituted cut tobacco using thick pulp method, an online automatic detection device is designed in the paper. A cut tobacco sampling and spreading out process is designed. Cut tobacco sampling is completed by using a robotic arm. Cut tobacco feeding is completed by simulating the manual shaking movement. A two-stage conveyor belt keeps spreading out tobacco leaves. A tobacco leaves image acquisition device have been designed to collect diluted cut tobacco. A software system has been developed, specifically for the case of a small amount of cross cut tobacco. A stitching algorithm based on sub pixel skeleton contour slope has been proposed to separate a single cut tobacco and facilitate tobacco length detection. The test results indicate that the system has the advantages of automation, online detection, and high measurement accuracy, which can meet the requirements of the production process of reconstituted tobacco leaves.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"100 1","pages":"129692A - 129692A-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development and maturity of UAV technology, its application fields are more and more extensive. In the actual flight process of UAV, itis necessary to calculate the safe path efficiently and reliably according to the external environment information. However, the existing UAV path planning algorithm has some limitations such as single applicable type, slow convergence speed, and easy to fall into the local optimum. The application of snake optimization algorithm in UAV path planning can solve the above problems. It can carry out fast and efficient path planning in complex environments, and the application of this algorithm provides a new direction for the problems related to the path planning of unmanned aerial vehicles.
{"title":"Research on UAV path planning based on snake optimization algorithm","authors":"Yan-Ping Fan, Meng-Yao Yao, Lin Li, Kai Yang","doi":"10.1117/12.3014691","DOIUrl":"https://doi.org/10.1117/12.3014691","url":null,"abstract":"With the development and maturity of UAV technology, its application fields are more and more extensive. In the actual flight process of UAV, itis necessary to calculate the safe path efficiently and reliably according to the external environment information. However, the existing UAV path planning algorithm has some limitations such as single applicable type, slow convergence speed, and easy to fall into the local optimum. The application of snake optimization algorithm in UAV path planning can solve the above problems. It can carry out fast and efficient path planning in complex environments, and the application of this algorithm provides a new direction for the problems related to the path planning of unmanned aerial vehicles.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"19 2","pages":"129690I - 129690I-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to enhance the efficiency and accuracy of homologous tampering detection, image segmentation algorithms and image feature points are combined. The Simple Linear Iterative Cluster (SLIC) algorithm is employed for image segmentation. However, manually presetting the number of patches is not applicable to all images and can influence subsequent segmentation results. To achieve a more accurate detection of tampered areas, this paper proposes a self adaptive image tampering detection algorithm. The number of image segments is determined based on image complexity, which allows the image to be segmented into semantically independent patches. Subsequently, the SIFT algorithm is employed to extract feature points for matching. Test results demonstrate that the proposed algorithm accurately localizes tampered regions and reduces algorithmic complexity.
{"title":"A self-adaptive tampering detection algorithm based on image segmentation and feature point matching","authors":"Guokai Wang, Liuping Feng, Lingyi Chi, Yangquan Zhou","doi":"10.1117/12.3014419","DOIUrl":"https://doi.org/10.1117/12.3014419","url":null,"abstract":"In order to enhance the efficiency and accuracy of homologous tampering detection, image segmentation algorithms and image feature points are combined. The Simple Linear Iterative Cluster (SLIC) algorithm is employed for image segmentation. However, manually presetting the number of patches is not applicable to all images and can influence subsequent segmentation results. To achieve a more accurate detection of tampered areas, this paper proposes a self adaptive image tampering detection algorithm. The number of image segments is determined based on image complexity, which allows the image to be segmented into semantically independent patches. Subsequently, the SIFT algorithm is employed to extract feature points for matching. Test results demonstrate that the proposed algorithm accurately localizes tampered regions and reduces algorithmic complexity.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"16 6","pages":"1296909 - 1296909-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most floating wind turbine foundations consist of single or multiple columns, which are prone to vortex-induced motion (VIM) under the action of uniform flow. VIM is the main reason causing fatigue damage to mooring structures. Exploring the motion response of VIM of the spar-type wind turbine structure, the spar wind turbine foundation was designed as a prototype, and the model was simplified to a cylinder. In this paper, we conducted an experimental study of its model in the circulating water tank based on Matlab image processing technology. The VIM characteristics of the model were discussed by capturing its optical measurement points, and then the structural response amplitude was obtained by using the target tracking method. The study showed that the optical measurement method could effectively monitor the motion response of the structure, and the binary method could better obtain the VIM characteristics of the structure. Meanwhile, it was found that the response amplitude of the cylindrical VIM presents four stages: initial excitation branch, upper branch, lower branch, and desynchronization. It was also revealed that the maximum amplitude was reached in the upper branch, and the locking phenomenon was shown at the reduced velocity 4.96 ≤ Ur ≤ 7.11.
大多数浮式风力涡轮机基础由单柱或多柱组成，在匀速气流作用下容易产生涡流诱导运动（VIM）。VIM 是造成系泊结构疲劳损坏的主要原因。为了探索吊杆式风力涡轮机结构的 VIM 运动响应，我们设计了吊杆式风力涡轮机基础作为原型，并将模型简化为圆柱体。本文基于 Matlab 图像处理技术，在循环水箱中对其模型进行了实验研究。通过捕捉光学测量点讨论了模型的 VIM 特性，然后使用目标跟踪方法获得了结构响应振幅。研究表明，光学测量方法能有效监测结构的运动响应，二元方法能更好地获得结构的 VIM 特性。同时，研究发现圆柱形 VIM 的响应振幅呈现四个阶段：初始激励分支、上分支、下分支和不同步。研究还发现，最大振幅出现在上分支，并在减速度 4.96 ≤ Ur ≤ 7.11 时出现锁定现象。
{"title":"Experimental study on vortex-induced motion of spar-type floating offshore wind turbine based on image processing technology","authors":"Yuhong Wang, haishan xia, Yanghong Xiao, Lei Li","doi":"10.1117/12.3014526","DOIUrl":"https://doi.org/10.1117/12.3014526","url":null,"abstract":"Most floating wind turbine foundations consist of single or multiple columns, which are prone to vortex-induced motion (VIM) under the action of uniform flow. VIM is the main reason causing fatigue damage to mooring structures. Exploring the motion response of VIM of the spar-type wind turbine structure, the spar wind turbine foundation was designed as a prototype, and the model was simplified to a cylinder. In this paper, we conducted an experimental study of its model in the circulating water tank based on Matlab image processing technology. The VIM characteristics of the model were discussed by capturing its optical measurement points, and then the structural response amplitude was obtained by using the target tracking method. The study showed that the optical measurement method could effectively monitor the motion response of the structure, and the binary method could better obtain the VIM characteristics of the structure. Meanwhile, it was found that the response amplitude of the cylindrical VIM presents four stages: initial excitation branch, upper branch, lower branch, and desynchronization. It was also revealed that the maximum amplitude was reached in the upper branch, and the locking phenomenon was shown at the reduced velocity 4.96 ≤ Ur ≤ 7.11.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":" 3","pages":"1296910 - 1296910-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the course of work, people are easily affected by the complexity of the environment, so it is difficult to ensure the assessment of personnel safety risks. In view of this situation, a safety risk assessment method of key personnel in infrastructure projects based on image data coupling identification is proposed. Under the coupling recognition of image data, a comprehensive evaluation model is established by combining dynamic graph reasoning technology, and the relevant signals of the working environment of key personnel in infrastructure projects are extracted from the model, and images are obtained on the projection surfaces of the left terminal and the right terminal, so as to locate the positions of key personnel in infrastructure projects and realize the safety risk assessment of key personnel in infrastructure projects. The experimental results show that the average channel collision rate of the proposed method is low, which can greatly improve the accuracy of dangerous action identification and has good performance and effect.
{"title":"Safety risk assessment method of key personnel in infrastructure projects based on image data coupling identification","authors":"Xiaodong Wang, Linyu Zhang, Zhiqiang Xu, Hui Xiao, Lixuan Guo","doi":"10.1117/12.3014393","DOIUrl":"https://doi.org/10.1117/12.3014393","url":null,"abstract":"In the course of work, people are easily affected by the complexity of the environment, so it is difficult to ensure the assessment of personnel safety risks. In view of this situation, a safety risk assessment method of key personnel in infrastructure projects based on image data coupling identification is proposed. Under the coupling recognition of image data, a comprehensive evaluation model is established by combining dynamic graph reasoning technology, and the relevant signals of the working environment of key personnel in infrastructure projects are extracted from the model, and images are obtained on the projection surfaces of the left terminal and the right terminal, so as to locate the positions of key personnel in infrastructure projects and realize the safety risk assessment of key personnel in infrastructure projects. The experimental results show that the average channel collision rate of the proposed method is low, which can greatly improve the accuracy of dangerous action identification and has good performance and effect.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"32 3","pages":"129690F - 129690F-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To address the challenges of manual transmission line inspection and UAV line-following flights, this study presents a systematic UAV line tracking method based on binocular vision. The proposed method involves several key steps. First, the camera captures images of the transmission lines during flight, and these visual inputs are continually updated in realtime. Second, the images undergo pre-processing using Gaussian blur and bilateral filtering algorithms to mitigate the impact of light and noise interference on image detection. Subsequently, the 3D nodes of the line within the image are detected and extracted using the Hough transform and BM algorithm. The 3D pose of the specified tracking line is then determined using the least squares algorithm. Lastly, a visual guidance strategy is presented for the UAV to effectively track the designated line. The real-time capability and accuracy of the method are validated through experimental verification.
{"title":"UAV transmission line transfer-acceptance technology based on vision","authors":"Sijiang Zhang, Zhengfa Li, Linke Huang, Kangwei Jia, Dongsheng Zhang","doi":"10.1117/12.3014392","DOIUrl":"https://doi.org/10.1117/12.3014392","url":null,"abstract":"To address the challenges of manual transmission line inspection and UAV line-following flights, this study presents a systematic UAV line tracking method based on binocular vision. The proposed method involves several key steps. First, the camera captures images of the transmission lines during flight, and these visual inputs are continually updated in realtime. Second, the images undergo pre-processing using Gaussian blur and bilateral filtering algorithms to mitigate the impact of light and noise interference on image detection. Subsequently, the 3D nodes of the line within the image are detected and extracted using the Hough transform and BM algorithm. The 3D pose of the specified tracking line is then determined using the least squares algorithm. Lastly, a visual guidance strategy is presented for the UAV to effectively track the designated line. The real-time capability and accuracy of the method are validated through experimental verification.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"28 3","pages":"129692S - 129692S-9"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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