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.
徐建军, 黄立达, 闫丽梅, 伊娜. 基于层次多任务深度学习的绝缘子自爆缺陷检测[J]. 电工技术学报, 2021, 36(7): 1407-1415.
Xu Jianjun, Huang Lida, Yan Limei, Yi Na. Insulator Self-Explosion Defect Detection Based on Hierarchical Multi-Task Deep Learning. Transactions of China Electrotechnical Society, 2021, 36(7): 1407-1415.
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2021, Vol. 36 
