[1] Lin Ying, Guo Zhihong, Chen Yufeng. Convolutional-recursive network based current transformer infrared fault image diagnosis[J]. Power System Protection and Control, 2015, 43(16): 87-94. (in Chinese)
[2] Wei Gang, Feng Zhongzheng, Tang Yue, et al. The infrared diagnostic technology of power transmission devices and experimental study[J]. Electrical Engineering, 2013, 14(6): 75-78. (in Chinese)
[3] Wang Jialin, Cui Haoyang, Xu Yong, et al. Infrared image diagnosis method of transformer substation equipment base on SOM neural network[J]. Journal of Shanghai University of Electric Power, 2016, 32(1): 78-82. (in Chinese)
[4] Zhang Difei, Zhang Jinsuo, Yao Keming, et al. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 0104001. (in Chinese)
[5] Xie Saining, Ross Girshick, Piotr Dollár, et al. Aggregated residual transformations for deep neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1492-1500.
[6] Wang Wanguo, Tian Bing, Liu Yue, et al. Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J]. Journal of Geo-Information Science, 2017, 19(2): 256-263. (in Chinese)
[7] Liu Bin, Zhang Jian. Partial discharge recogniton in power transformers based on convolutional neural networks[J]. High Voltage Apparatus, 2017, 53(5): 70-74. (in Chinese)
[8] Wang Juan, Wang Ping, Wang Gang. Stippled direct part mark location based on self-adaptive super-pixels segmentation[J]. Acta Automatica Sinica, 2015, 41(5): 991-1003. (in Chinese)
[9] Liu Wei, Christian Szegedy, Jia Yangqing, et al. Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[10] Wen Yandong, Zhang Kaipeng, Li Zhifeng, et al. A discriminative feature learning approach for deep face recognition[C]//European Conference on Computer Vision, 2016: 499-515.
