EL IMAGE DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV5

Zheng Zonghui; Ma Pengge

doi:10.19912/j.0254-0096.tynxb.2024-1101

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 295-301. DOI: 10.19912/j.0254-0096.tynxb.2024-1101

EL IMAGE DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV5

Zheng Zonghui, Ma Pengge

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Abstract

The complex background interference in electroluminescence（EL） images of photovoltaic modules poses challenges for accurate defect identification. To address this, an improved deep learning model YOLOV5-PMD based on YOLOv5 is proposed. This model utilizes a lightweight convolution GSConv module to replace the Conv part of the Neck, enhancing accuracy while reducing the number of parameters. In addition, the convolutional CBAM attention mechanism is combined to improve the feature perception ability, and the EIoU loss function is replaced by the CIoU loss function to achieve more accurate defect location. On the public SolarMonocrystal dataset, the improved model reduces the training parameters from 7.025×10⁶ to 6.695×10⁶, and the average accuracy of mAP50 reaches 89.8%, which is 2.6% higher than that of the original model.

Key words

photovoltaic modules / defect / deep learning / attention mechanism / YOLOv5 / loss function

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Zheng Zonghui, Ma Pengge. EL IMAGE DEFECT DETECTION OF PHOTOVOLTAIC MODULES BASED ON IMPROVED YOLOV5[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 295-301 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1101

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