Electroluminescence Image Enhancement Method for Photovoltaic Panels

To address the issues of loss of defect details, low brightness, and indistinct defect features on photovoltaic (PV) panel images caused by variations in electroluminescence radiation intensity during defect detection, an improved CycleGAN-based infrared image enhancement method is proposed. A generator with an Auto-Encoder network structure featuring a self-attention mechanism is designed to address the poor feature extraction capability of the original network structure. A Unet-structured network is proposed for the discriminator to replace the original structure, allowing the discriminator to train independently of the generator. The training ratio between the generator and discriminator is adjusted to 1:3 to enhance the guidance effect of the discriminator on the generator. Experimental results show that the proposed algorithm outperforms existing image enhancement algorithms on the PVEL-AD dataset in both qualitative and quantitative evaluations. It effectively enhances the quality, clarity, and defect feature details of the electroluminescence images of PV panels. In practical detection processes, it improves the mAP50, mAP50:95, and recall rate by 3.9%, 3.5%, and 4.2%, respectively, further demonstrating the effectiveness of the proposed method.