Foreign Object Detection on Insulators Based on Improved YOLO v3
As an important component of transmission lines, insulator plays an essential role in the stable operation of the power grid. However, the outdoor environment in which the insulators are located can easily lead to the hanging of foreign objects. This paper proposes a novel method for foreign object...
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| Language: | zho |
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State Grid Energy Research Institute
2020-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201908009 |
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| author | Huankun ZHANG Junyi LI Bin ZHANG |
| author_facet | Huankun ZHANG Junyi LI Bin ZHANG |
| author_sort | Huankun ZHANG |
| collection | DOAJ |
| description | As an important component of transmission lines, insulator plays an essential role in the stable operation of the power grid. However, the outdoor environment in which the insulators are located can easily lead to the hanging of foreign objects. This paper proposes a novel method for foreign object detection on insulators based on the improved YOLO v3: Dense-YOLO v3. A dense network is designed to replace one of the convolutional layers of the original network in order to realize the multi-layer feature reuse and fusion of the insulator, which improves the detection accuracy. In addition, we amplify the training set to improve the training effect of the network and propose a wrong detection cost function to measure the risk of false detection. The experiment shows that the proposed algorithm has a detection precision rate reaching up to 94.54%. Meanwhile, the Dense-YOLO v3 outperforms YOLO v3 and Faster R-CNN, both in terms of detection accuracy and wrong detection cost. The result shows that the presented approach can be applied to the UAV inspection of transmission lines. |
| format | Article |
| id | doaj-art-02a7ca83517140f7b42b62dfd4ffc1c8 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2020-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-02a7ca83517140f7b42b62dfd4ffc1c82025-08-20T02:52:37ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492020-02-01532495510.11930/j.issn.1004-9649.201908009zgdl-52-12-zhanghuankunForeign Object Detection on Insulators Based on Improved YOLO v3Huankun ZHANG0Junyi LI1Bin ZHANG2School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaAs an important component of transmission lines, insulator plays an essential role in the stable operation of the power grid. However, the outdoor environment in which the insulators are located can easily lead to the hanging of foreign objects. This paper proposes a novel method for foreign object detection on insulators based on the improved YOLO v3: Dense-YOLO v3. A dense network is designed to replace one of the convolutional layers of the original network in order to realize the multi-layer feature reuse and fusion of the insulator, which improves the detection accuracy. In addition, we amplify the training set to improve the training effect of the network and propose a wrong detection cost function to measure the risk of false detection. The experiment shows that the proposed algorithm has a detection precision rate reaching up to 94.54%. Meanwhile, the Dense-YOLO v3 outperforms YOLO v3 and Faster R-CNN, both in terms of detection accuracy and wrong detection cost. The result shows that the presented approach can be applied to the UAV inspection of transmission lines.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201908009insulatorneural networkdense-netforeign object detectionyolo v3 |
| spellingShingle | Huankun ZHANG Junyi LI Bin ZHANG Foreign Object Detection on Insulators Based on Improved YOLO v3 Zhongguo dianli insulator neural network dense-net foreign object detection yolo v3 |
| title | Foreign Object Detection on Insulators Based on Improved YOLO v3 |
| title_full | Foreign Object Detection on Insulators Based on Improved YOLO v3 |
| title_fullStr | Foreign Object Detection on Insulators Based on Improved YOLO v3 |
| title_full_unstemmed | Foreign Object Detection on Insulators Based on Improved YOLO v3 |
| title_short | Foreign Object Detection on Insulators Based on Improved YOLO v3 |
| title_sort | foreign object detection on insulators based on improved yolo v3 |
| topic | insulator neural network dense-net foreign object detection yolo v3 |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201908009 |
| work_keys_str_mv | AT huankunzhang foreignobjectdetectiononinsulatorsbasedonimprovedyolov3 AT junyili foreignobjectdetectiononinsulatorsbasedonimprovedyolov3 AT binzhang foreignobjectdetectiononinsulatorsbasedonimprovedyolov3 |