[1] Yahya Z, Imane S, Hicham H et al. Applied imagery pattern recognition for photovoltaic modules’ inspection: a review on methods, challenges and future development[J]. Sustainable Energy Technologies and Assessments, 52, 102071(2022).

[2] Duan Z X, Zhang Y M, Ma J H. Infrared image recognition of power equipment based on improved YOLOv4[J]. Laser & Optoelectronics Progress, 59, 2410002(2022).

[3] Gu Y, Li Z, Yang F et al. Infrared vehicle detection algorithm with complex background based on improved Faster R-CNN[J]. Laser & Infrared, 52, 614-619(2022).

[4] Mei J H, Yun L J, Zhu X P. Infrared human gait recognition method based on long and short term memory network[J]. Laser & Optoelectronics Progress, 59, 0811005(2022).

[5] He Z F, Chen G C, Chen J S et al. Multi-scale feature fusion lightweight real-time infrared pedestrian detection at night[J]. Chinese Journal of Lasers, 49, 1709002(2022).

[6] Haidari P, Hajiahmad A, Jafari A et al. Deep learning-based model for fault classification in solar modules using infrared images[J]. Sustainable Energy Technologies and Assessments, 52, 102110(2022).

[7] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[8] Hwang H P C, Ku C C Y, Chan J C C. Detection of malfunctioning photovoltaic modules based on machine learning algorithms[J]. IEEE Access, 9, 37210-37219(2021).

[9] Jiang P, Li M Y, Luan Y J. Fault classification method of photovoltaic module aerial infrared image based on improved Inceptionv3 network[J]. Laser Journal, 43, 90-94(2022).

[10] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[11] Alves R H F, de Deus G A,, Marra E G et al. Automatic fault classification in photovoltaic modules using Convolutional Neural Networks[J]. Renewable Energy, 179, 502-516(2021).

[12] Wang Y, Shen Z W, Zhao H S et al. Infrared image fault diagnosis method of photovoltaic module based on improved CNN-SVM[J/OL]. Journal of North China Electric Power University (Natural Science Edition), 1-8. http://kns.cnki.net/kcms/detail/13.1212.TM.20220801.1622.004.html

[13] Le M, Luong V S, Nguyen D K et al. Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network[J]. Sustainable Energy Technologies and Assessments, 48, 101545(2021).

[14] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[EB/OL]. https://arxiv.org/abs/1603.05027

[15] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).

[16] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2022).

[17] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C](2017).

[18] Millendorf M, Obropta E, Vadhavkar N. Infrared solar module dataset for anomaly detection[EB/OL]. https://ai4earthscience.github.io/iclr-2020-workshop/papers/ai4earth22.pdf

[19] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[EB/OL]. https://arxiv.org/abs/1807.11164

[20] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[21] Tan M, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. https://arxiv.org/abs/1905.11946