Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1207 |
| _version_ | 1827661683131154432 |
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| author | Zhibin Qiu Xuan Zhu Caibo Liao Dazhai Shi Wenqian Qu |
| author_facet | Zhibin Qiu Xuan Zhu Caibo Liao Dazhai Shi Wenqian Qu |
| author_sort | Zhibin Qiu |
| collection | DOAJ |
| description | Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines. |
| first_indexed | 2024-03-10T00:14:39Z |
| format | Article |
| id | doaj.art-0d83042ee01a42d3b977d4bd96c84a36 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:14:39Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0d83042ee01a42d3b977d4bd96c84a362023-11-23T15:53:46ZengMDPI AGApplied Sciences2076-34172022-01-01123120710.3390/app12031207Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 ModelZhibin Qiu0Xuan Zhu1Caibo Liao2Dazhai Shi3Wenqian Qu4Department of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, ChinaDefective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.https://www.mdpi.com/2076-3417/12/3/1207transmission lineinsulatorimproved YOLOv4lightweight convolutional neural networkdefect detection |
| spellingShingle | Zhibin Qiu Xuan Zhu Caibo Liao Dazhai Shi Wenqian Qu Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model Applied Sciences transmission line insulator improved YOLOv4 lightweight convolutional neural network defect detection |
| title | Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model |
| title_full | Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model |
| title_fullStr | Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model |
| title_full_unstemmed | Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model |
| title_short | Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model |
| title_sort | detection of transmission line insulator defects based on an improved lightweight yolov4 model |
| topic | transmission line insulator improved YOLOv4 lightweight convolutional neural network defect detection |
| url | https://www.mdpi.com/2076-3417/12/3/1207 |
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