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| Research on infrared image missing insulator detection method based on deep learning |
| DOI：DOI: 10.19783/j.cnki.pspc.200950 |
| Key Words:insulator defect image processing Fast R-CNN algorithm model training |
| Author Name | Affiliation | | HE Ninghui1 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China | | WANG Shijie2 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China | | LIU Junfu3 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China | | ZHANG Hao4 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China | | WU Liangfang3 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China | | ZHOU Xiu1 | 1. State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
2. State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750011, China
3. Zhongwei Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Zhongwei
751700, China
4. Yinchuan Power Supply Company, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750011, China |
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| Abstract:There is a problem of low efficiency of manual processing and analysis of UAV inspection images, coupled with the large influence of human factors on detection results. Thus a missing insulator recognition method based on image recognition is proposed. First, the image samples taken by the UAV are processed to expand the sample set. Secondly, an insulator detection model is built to complete the selection and design of the network structure at each layer, and a CNN algorithm is used to detect the missing insulators. Subsequently, an insulator detection network is constructed, and parameters of each layer of the detection network are configured. Images actually taken are selected as training samples for network training. The test results have confirmed that several indices are above 0.95, indicating that the algorithm could accurately identify insulators. Finally, the CNN algorithm is used to perform missing insulator detection on aerial insulators. The correct identification rate of missing defects in the insulation sheet is 86%. The algorithm could automatically display whether the insulators have missing defects according to the detection results.This work is supported by the Ningxia Natural Science Foundation (No. 2018AAC03222). |
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