High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12682 |
| _version_ | 1797461647638396928 |
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| author | Yourui Huang Lingya Jiang Tao Han Shanyong Xu Yuwen Liu Jiahao Fu |
| author_facet | Yourui Huang Lingya Jiang Tao Han Shanyong Xu Yuwen Liu Jiahao Fu |
| author_sort | Yourui Huang |
| collection | DOAJ |
| description | As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection. |
| first_indexed | 2024-03-09T17:22:24Z |
| format | Article |
| id | doaj.art-b956e061e9dd40e59af7f2047fb10543 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:22:24Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b956e061e9dd40e59af7f2047fb105432023-11-24T13:02:54ZengMDPI AGApplied Sciences2076-34172022-12-0112241268210.3390/app122412682High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5Yourui Huang0Lingya Jiang1Tao Han2Shanyong Xu3Yuwen Liu4Jiahao Fu5School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaAs a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.https://www.mdpi.com/2076-3417/12/24/12682insulator detectionYOLOv5small target detectionmulti-scale edge detectionreceptive field module |
| spellingShingle | Yourui Huang Lingya Jiang Tao Han Shanyong Xu Yuwen Liu Jiahao Fu High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 Applied Sciences insulator detection YOLOv5 small target detection multi-scale edge detection receptive field module |
| title | High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 |
| title_full | High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 |
| title_fullStr | High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 |
| title_full_unstemmed | High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 |
| title_short | High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5 |
| title_sort | high accuracy insulator defect detection for overhead transmission lines based on improved yolov5 |
| topic | insulator detection YOLOv5 small target detection multi-scale edge detection receptive field module |
| url | https://www.mdpi.com/2076-3417/12/24/12682 |
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