Transmission line bolts and their defects detection method based on position relationship
Introduction: To solve the problems of small proportion of bolts in aerial images of power transmission lines, small differences between classes, and difficulty in extracting refined features, this paper proposes a method for detecting power transmission line bolts and their defects based on positio...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1269087/full |
| _version_ | 1827818287729213440 |
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| author | Zhenbing Zhao Zhenbing Zhao Zhenbing Zhao Jing Xiong Jing Xiong Yu Han Siyu Miao |
| author_facet | Zhenbing Zhao Zhenbing Zhao Zhenbing Zhao Jing Xiong Jing Xiong Yu Han Siyu Miao |
| author_sort | Zhenbing Zhao |
| collection | DOAJ |
| description | Introduction: To solve the problems of small proportion of bolts in aerial images of power transmission lines, small differences between classes, and difficulty in extracting refined features, this paper proposes a method for detecting power transmission line bolts and their defects based on positional relationships.Methods: Firstly, a spatial attention module is added to Faster R-CNN, using two parallel cross attention to obtain cross path features and global features respectively, and spatial feature enhancement is performed on the features output from the convolution layer. Then, starting from the spatial position relationship of bolts and their defects, using the relative geometric features of candidate regions as input, the spatial position relationship of bolts and their defects on the image is modeled. Finally, the position features and regional features are connected to obtain enhanced features. The bolt position knowledge on the connecting plate is added to the detection model to improve the detection accuracy of the model.Results and discussion: The experimental results show that the mAP value of the algorithm in this paper is increased by 6.61% compared to the Faster R-CNN detection model in aerial photography of transmission line bolts and their defect datasets, with the AP value of normal bolts increased by 1.73%, the AP value of pin losing increased by 4.45%, and the AP value of nut losing increased by 13.63%. |
| first_indexed | 2024-03-12T00:45:42Z |
| format | Article |
| id | doaj.art-00c51df987fc4c439ad0a7d7250fbb91 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-12T00:45:42Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-00c51df987fc4c439ad0a7d7250fbb912023-09-14T15:52:32ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-09-011110.3389/fenrg.2023.12690871269087Transmission line bolts and their defects detection method based on position relationshipZhenbing Zhao0Zhenbing Zhao1Zhenbing Zhao2Jing Xiong3Jing Xiong4Yu Han5Siyu Miao6School of Electrical and Electronic Engineering, North China Electric Power University, Baoding, ChinaEngineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding, ChinaHebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding, ChinaDepartment of Information Engineering, Sichuan Vocational and Technical College of Communications, Chengdu, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding, ChinaIntroduction: To solve the problems of small proportion of bolts in aerial images of power transmission lines, small differences between classes, and difficulty in extracting refined features, this paper proposes a method for detecting power transmission line bolts and their defects based on positional relationships.Methods: Firstly, a spatial attention module is added to Faster R-CNN, using two parallel cross attention to obtain cross path features and global features respectively, and spatial feature enhancement is performed on the features output from the convolution layer. Then, starting from the spatial position relationship of bolts and their defects, using the relative geometric features of candidate regions as input, the spatial position relationship of bolts and their defects on the image is modeled. Finally, the position features and regional features are connected to obtain enhanced features. The bolt position knowledge on the connecting plate is added to the detection model to improve the detection accuracy of the model.Results and discussion: The experimental results show that the mAP value of the algorithm in this paper is increased by 6.61% compared to the Faster R-CNN detection model in aerial photography of transmission line bolts and their defect datasets, with the AP value of normal bolts increased by 1.73%, the AP value of pin losing increased by 4.45%, and the AP value of nut losing increased by 13.63%.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1269087/fulltransmission line boltsbolts defectstarget detectionattention mechanismpositional relationship |
| spellingShingle | Zhenbing Zhao Zhenbing Zhao Zhenbing Zhao Jing Xiong Jing Xiong Yu Han Siyu Miao Transmission line bolts and their defects detection method based on position relationship Frontiers in Energy Research transmission line bolts bolts defects target detection attention mechanism positional relationship |
| title | Transmission line bolts and their defects detection method based on position relationship |
| title_full | Transmission line bolts and their defects detection method based on position relationship |
| title_fullStr | Transmission line bolts and their defects detection method based on position relationship |
| title_full_unstemmed | Transmission line bolts and their defects detection method based on position relationship |
| title_short | Transmission line bolts and their defects detection method based on position relationship |
| title_sort | transmission line bolts and their defects detection method based on position relationship |
| topic | transmission line bolts bolts defects target detection attention mechanism positional relationship |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1269087/full |
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