基于层次多任务深度学习的绝缘子自爆缺陷检测

doi:10.19595/j.cnki.1000-6753.tces.L90049

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Abstract Insulators are important and widely used devices in power lines. With the rapid popularization of unmanned aerial vehicles in recent years, detecting insulator self-explosion defect from aerial images has become a hot issue. In aerial images, distinguishing self-explosion insulators from normal insulators is more difficult than from other contents. The paper proposed an insulator self-explosion defect detection model based on hierarchical multi-task deep learning, using a dedicated convolutional neural network to distinguish self-explosion insulators from normal insulators. To improve the classification accuracy, multi-task learning and feature fusion method were used in the hierarchical model. Furthermore, in view of the lack of self-explosion insulator data, the paper proposed to use synthetic pictures for data augmentation. The experimental results show that adding synthetic pictures can effectively improve the recall rate of self-explosion class, and the hierarchical multi-task learning model achieves stronger classification performance than the flat classification model and the ordinary hierarchical model.

Key words： Insulator multi-task learning defect detection deep learning hierarchical classification

Received: 10 June 2020

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TP216

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	Xu Jianjun
	Huang Lida
	Yan Limei
	Yi Na

Cite this article:

Xu Jianjun,Huang Lida,Yan Limei等. Insulator Self-Explosion Defect Detection Based on Hierarchical Multi-Task Deep Learning[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1407-1415.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90049 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I7/1407

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