Online insulator defects detection and application based on YOLOv7-tiny algorithm
As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an ins...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1372618/full |
| _version_ | 1797261934065614848 |
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| author | Sheng Wu Xiangyan Gan Jian Xiao Cong Ma Tianyi Deng Zhibin Du Wei Qiu |
| author_facet | Sheng Wu Xiangyan Gan Jian Xiao Cong Ma Tianyi Deng Zhibin Du Wei Qiu |
| author_sort | Sheng Wu |
| collection | DOAJ |
| description | As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7-tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects. |
| first_indexed | 2024-04-24T23:49:06Z |
| format | Article |
| id | doaj.art-c9d5642d1aee4dc9985b96c199f3e72b |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-24T23:49:06Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-c9d5642d1aee4dc9985b96c199f3e72b2024-03-15T04:37:17ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-03-011210.3389/fenrg.2024.13726181372618Online insulator defects detection and application based on YOLOv7-tiny algorithmSheng Wu0Xiangyan Gan1Jian Xiao2Cong Ma3Tianyi Deng4Zhibin Du5Wei Qiu6Electric Power Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, ChinaHunan Xiangdian Experimental Research Institute Co., Ltd., Changsha, ChinaElectric Power Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha, ChinaSchool of Electrical and Information Engineering, Hunan University, Changsha, ChinaAs an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7-tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1372618/fullinsulator defectYOLOv7-tiny modeledge computing moduleonline detectiondrone inspection |
| spellingShingle | Sheng Wu Xiangyan Gan Jian Xiao Cong Ma Tianyi Deng Zhibin Du Wei Qiu Online insulator defects detection and application based on YOLOv7-tiny algorithm Frontiers in Energy Research insulator defect YOLOv7-tiny model edge computing module online detection drone inspection |
| title | Online insulator defects detection and application based on YOLOv7-tiny algorithm |
| title_full | Online insulator defects detection and application based on YOLOv7-tiny algorithm |
| title_fullStr | Online insulator defects detection and application based on YOLOv7-tiny algorithm |
| title_full_unstemmed | Online insulator defects detection and application based on YOLOv7-tiny algorithm |
| title_short | Online insulator defects detection and application based on YOLOv7-tiny algorithm |
| title_sort | online insulator defects detection and application based on yolov7 tiny algorithm |
| topic | insulator defect YOLOv7-tiny model edge computing module online detection drone inspection |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1372618/full |
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