RESEARCH ON DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV8

Cui Jianwei; Wang Yueming

doi:10.19912/j.0254-0096.tynxb.2024-0913

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 189-196. DOI: 10.19912/j.0254-0096.tynxb.2024-0913

RESEARCH ON DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV8

Cui Jianwei, Wang Yueming

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Abstract

Aiming at the problem of detecting bird droppings and shading defects as well as light spot defects formed by diode damage in photovoltaic modules under aerial high-resolution images, a detection algorithm combining the improves deep learning model YOLOv8n with slice-assisted super-reasoning is proposed. The PV module dataset is constructed by slicing 2667 visible and thermal imaging images and labelling them with three kinds of defects： bird droppings, shading and light spots. Firstly, on the backbone network of YOLOv8n, a small target detection layer is constructed by adding a 3-layer CBS module to enhance the transmission of small target feature information. Secondly, a global attention mechanism is added to the Backbone part, adopting the framework of channel and spatial attention, so that the model can better capture the global feature information. Based on the above two improvements to the network structure to design its ablation experiments, the experimental results show that the improved model improves the mAP50 value and mAP50：95 by 4.3% and 1.5%, respectively, compared with the base model; to design the comparison experiments between the improved model and the other target detection models, and the experimental results show that the improved model's accuracy is better than that of the other detection models. Finally, combined with slice-assisted hyper-reasoning for slicing before detection, the overall model comparison experiments are designed with the addition of slice-assisted hyper-reasoning, and the experimental results show that the improved YOLOv8-PG+SAHI model is optimal for detecting small target defects in high-resolution photovoltaic module images, and the accuracy rate can reach 88.73%. The above experiments show that the improved model is more suitable for small target detection of PV modules under aerial high-resolution images.

Key words

PV modules / defects / deep learning / high-resolution images / small target detection

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Cui Jianwei, Wang Yueming. RESEARCH ON DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV8[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 189-196 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0913

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