Research on defect recognition method of substation inspection images based on faster R-CNN

Xinxi Yu; Xian Meng; Xiping Jiang; Zhu Zhu; Xiaoping Li

doi:10.1117/12.2628731

22 April 2022 Research on defect recognition method of substation inspection images based on faster R-CNN

Xinxi Yu, Xian Meng, Xiping Jiang, Zhu Zhu, Xiaoping Li

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121741F (2022) https://doi.org/10.1117/12.2628731
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Aiming at the problems of low efficiency and poor accuracy in manual identification of tens of thousands of substation inspection images in the power grid, one method and system for identifying defects in substation inspection image data based on Faster R-CNN is proposed. The inspection images are preprocessed through data analysis module, image annotation module, image cleaning module, firstly. A convolutional neural network (CNN) is used to perform feature extraction on the processed images to obtain the target features, secondly. A Region Proposal Network (RPN) is introduced, which generates multiple candidate anchor frames on the target feature images, thirdly. Region of Interest (ROI) pooling is performed on the multiple candidate anchor frames to obtain a feature matrix of a fixed size, fourthly. Then, the frame regression and classification recognition are applied to obtain the defect recognition result and target anchor frame position. Finally, high-precision identification of 25 types of defects of substation inspection images such as oil leakage, insulator damage, mark damage, metal corrosion， is realized. The average accuracy of the method is as high as 93.44%, which is about 27% and 23% higher than the traditional systems with R-CNN and Fast R-CNN algorithms. The time of single image recognition is shortened to (150ms) millisecond level. This method greatly saves the detection time, which can effectively reduce manpower input, improve work efficiency more effectively, and provide a foundation for the digital transformation of the power grid.

Citation Download Citation

Xinxi Yu, Xian Meng, Xiping Jiang, Zhu Zhu, and Xiaoping Li "Research on defect recognition method of substation inspection images based on faster R-CNN", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121741F (22 April 2022); https://doi.org/10.1117/12.2628731

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KEYWORDS

Inspection

Detection and tracking algorithms

Defect inspection

Feature extraction

Evolutionary algorithms

Image processing

Data analysis

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