Deep-Learning-Based Detection of Transmission Line Insulators

Jian Zhang, Tian Xiao, Minhang Li and Yucai Zhou ()
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Jian Zhang: School of Design and Art, Changsha University of Science & Technology, Changsha 410114, China
Tian Xiao: School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Minhang Li: School of Design and Art, Changsha University of Science & Technology, Changsha 410114, China
Yucai Zhou: School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Energies, 2023, vol. 16, issue 14, 1-17

Abstract: At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications.

Keywords: insulator; image processing; deep learning; target identification; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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