DEFECT DETECTION MODEL OF INFRARED PHOTOVOLTAIC MODULE BASED ON KNOWLEDGE DISTILLATION

Wang Yin; Zhang Jie; Xie Gang; Zhao Zhicheng; Hu Xiao; Wu Xiaohui

doi:10.19912/j.0254-0096.tynxb.2024-0402

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 653-662. DOI: 10.19912/j.0254-0096.tynxb.2024-0402

Special Topics of Academic Papers at the 97th Annual Meeting of the China Association for Science and Technology

DEFECT DETECTION MODEL OF INFRARED PHOTOVOLTAIC MODULE BASED ON KNOWLEDGE DISTILLATION

Wang Yin^1,2, Zhang Jie^1,2, Xie Gang^1,2, Zhao Zhicheng^1,2, Hu Xiao^1,2, Wu Xiaohui³

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Abstract

Aiming at the problem that the infrared photovoltaic module defect detection model is difficult to deploy to edge devices due to the increasing number of parameters and computational complexity of object detection model, a defect detection algorithm T-DINO （Tiny DINO） based on model compression is proposed. Using ResNet-101 as the teacher network and ResNet-18 as the student network, a dynamic adaptive distillation method is proposed. The difference in attention weights between the two is used for efficient knowledge transfer in feature-based distillation, and it is also used as guiding knowledge for distillation of the student network in output response （logit） distillation. Consequently, the complexity of the model and the number of parameters are greatly reduced with minimal accuracy loss. At the same time, the fusion module CSF Block （Conv-Self Attention Block） is proposed to model local features and global features to improve the detection accuracy. Experiments on the self-constructed infrared PV module fault dataset showed a 77.3% reduction in the number of parameters, a 69.3% reduction in computational complexity and a 5.2% increase in AP50 compared to the baseline network DINO （ResNet-101） model. The simulation results show that the compressed model is suitable for deployment in edge equipment and can meet the requirements of actual infrared photovoltaic module defect detection.

Key words

defect detection / DINO / knowledge distillation / model compression / edge equipment

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Wang Yin, Zhang Jie, Xie Gang, Zhao Zhicheng, Hu Xiao, Wu Xiaohui. DEFECT DETECTION MODEL OF INFRARED PHOTOVOLTAIC MODULE BASED ON KNOWLEDGE DISTILLATION[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 653-662 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0402

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