Two Stage Insulator Fault Detection Method Based on Collaborative Deep Learning
Wang Zhuo1, Wang Yujing1, Wang Qingyan1, Kang Shouqiang1, V. I. Mikulovich2
1. School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbin 150080 China; 2. Belarusian State University Minsk 220030 Belorussia
Abstract For the problem of low accuracy caused by complex background interference in aerial images of existing insulator fault detection models, a two-stage insulator fault detection method based on collaborative deep learning is proposed. The method combines fully convolutional networks (FCN) with YOLOv3 target detection algorithm. The first stage, the FCN algorithm is used to preprocess the aerial image. The jump structure is designed to fuse the shallow image features and deep semantic features, and an 8-fold up-sampling insulator segmentation model is constructed. Combining with the image pixel logic operation, the initial segmentation of insulator target can be realized to avoid the interference of background area on insulator fault detection. On this basis, the second stage, YOLOv3 model is constructed to detect the insulator faults. The deep neural network Darknet-53 is used as the feature extractor, and referring to the idea of feature pyramid, the insulator faults can be marked and classified on three scales of output tensors to ensure the accurate detection of insulator faults of different sizes. K-means++ clustering algorithm is used to optimize the anchor boxes parameters of YOLOv3 to further improve the detection accuracy. The experimental results show that the two-stage method based on collaborative deep learning can effectively overcome the interference of complex background, the mean average precision of insulator fault detection is as high as 96.88%, which is 4.65% higher than the original YOLOv3 algorithm.
Wang Zhuo,Wang Yujing,Wang Qingyan等. Two Stage Insulator Fault Detection Method Based on Collaborative Deep Learning[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3594-3604.
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