Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former
The transmission lines are complex in distribution and it is difficult to effectively detect their faults. Among them, the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments. Aiming at the problem that the transmission line connection...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-06-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305035 |
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| _version_ | 1850037957555126272 |
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| author | Zhiwei SONG Xinbo HUANG Chao JI Fan ZHANG Ye ZHANG |
| author_facet | Zhiwei SONG Xinbo HUANG Chao JI Fan ZHANG Ye ZHANG |
| author_sort | Zhiwei SONG |
| collection | DOAJ |
| description | The transmission lines are complex in distribution and it is difficult to effectively detect their faults. Among them, the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments. Aiming at the problem that the transmission line connection fitting components are varied in scale and have poor accuracy in detecting their corrosion faults, a detection method is proposed for transmission line connection fittings and their corrosion faults based on dual attention embedding reconstruction and Swin Transformer, i.e., PCSA-YOLOv7 Former. The experimental results show that the proposed method is superior to 12 existing state-of-the-art object detection algorithms in comprehensive detection performance of the constructed TLCF dataset, with the mAP0.5 of the test set reaching 94.9 %. Compared with the baseline model YOLOv7, the proposed method improves the indexes F1 and mAP0.5 by 2.6 percentage points and 2.2 percentage points, respectively, indicating that the proposed method can more comprehensively understand the multi-scale semantic information in the images of transmission line connection fittings and learn their subtle details that are difficult to distinguish. |
| format | Article |
| id | doaj-art-0305c176c2854c56b4bd011735068db3 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-0305c176c2854c56b4bd011735068db32025-08-20T02:56:44ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-06-0157614115210.11930/j.issn.1004-9649.202305035zgdl-57-01-songzhiweiTransmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 FormerZhiwei SONG0Xinbo HUANG1Chao JI2Fan ZHANG3Ye ZHANG4School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaThe transmission lines are complex in distribution and it is difficult to effectively detect their faults. Among them, the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments. Aiming at the problem that the transmission line connection fitting components are varied in scale and have poor accuracy in detecting their corrosion faults, a detection method is proposed for transmission line connection fittings and their corrosion faults based on dual attention embedding reconstruction and Swin Transformer, i.e., PCSA-YOLOv7 Former. The experimental results show that the proposed method is superior to 12 existing state-of-the-art object detection algorithms in comprehensive detection performance of the constructed TLCF dataset, with the mAP0.5 of the test set reaching 94.9 %. Compared with the baseline model YOLOv7, the proposed method improves the indexes F1 and mAP0.5 by 2.6 percentage points and 2.2 percentage points, respectively, indicating that the proposed method can more comprehensively understand the multi-scale semantic information in the images of transmission line connection fittings and learn their subtle details that are difficult to distinguish.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305035transmission line connection fittingspcsa-yolov7 formerdual attention embeddingswin transformeratrous spatial pyramid pooling |
| spellingShingle | Zhiwei SONG Xinbo HUANG Chao JI Fan ZHANG Ye ZHANG Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former Zhongguo dianli transmission line connection fittings pcsa-yolov7 former dual attention embedding swin transformer atrous spatial pyramid pooling |
| title | Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former |
| title_full | Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former |
| title_fullStr | Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former |
| title_full_unstemmed | Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former |
| title_short | Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former |
| title_sort | transmission line connection fittings and corrosion detection method based on pcsa yolov7 former |
| topic | transmission line connection fittings pcsa-yolov7 former dual attention embedding swin transformer atrous spatial pyramid pooling |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305035 |
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