融合深度学习的无人机巡检绝缘子自爆检测研究

doi:10.3969/j.issn.0255-8297.2021.02.004

Abstract

Abstract: Insulator self-exploding detection is an important part of UAV inspection. Accurate, rapid and automatic searching for insulator self-exploding areas can greatly save the workload of UAV inspection data processing and improve inspection accuracy and efficiency. Aiming at the problem of low sample size, small target and low precision in the current insulator self-exploding detection, this paper proposes a deep learning self-exploding detection method for UAV inspection insulators. The method uses a large number of collected insulator samples to train the deep learning detection model, and then uses the computer vision method to detect the self-exploding region in the detected insulator. The method of this paper synthesizes the advantages of deep learning in detecting complex targets and the fact that computer vision does not require a large number of samples and can detect small targets. Experiments show that the detection accuracy of this algorithm can reach 84.8%. It has positive significance and application value for insulator self-exploding detection.

Key words: insulator, insulator self-exploding, deep learning, object detection

CLC Number:

P237

WANG Wanguo, MU Shiyou, LIU Yue, LIU Guangxiu, LANG Fenling. Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning[J]. Journal of Applied Sciences, 2021, 39(2): 222-231.

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Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning

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