| Citation: | XU Xiaohua, QIAN Ping, WANG Yida, et al. Multi-granularity hazard detection method for electrical power system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 520-530. doi: 10.13700/j.bh.1001-5965.2020.0491(in Chinese)
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As the security hazard of the electrical power system can lead to serious economic damage and social impacts, the potential hazard detection has become an indispensable part for electrical power system. With the advances of artificial intelligence, intelligent deep learning based hazard detection methods for electrical power system have emerged. Although the existing methods have made promising progress, most of them only consider the global or local features of the image, which cannot thoroughly characterize the imageand accurately conduct the hazard detection in the context of the electrical power system especially for the complex outdoor background. In the light of this, in this paper, we present a multi-granularity hazard detection network MGNet for the electrical power system. To be specific, we explore the multi-granularity representation of images with both the global and local representation learning networks. Based on that, we conduct the hazard detection at different granularity levels and finally collaboratively fuse the detection results to fulfill the precise hazard detection. Extensive experiments on two real-world datasets of hazard(i.e., tower connection fitting hazard dataset and transmission line channel mechanical hazard dataset) demonstrate the superiority of the detection performance of the proposed model. In particular, the mean average precision is improved by 2.74% and 2.77% on two datasets, respectively, compared with the existing optimal hazard detection benchmark method.
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