Defect analysis using electroluminescence imaging in photovoltaic modules

Photovoltaic (PV) technology plays a pivotal role in the conversion of solar energy into electricity. However, subtle defects in solar cells within PV modules can significantly impact both the energy efficiency and the lifespan of these modules. Accurate identification of such defects is essential to ensure measurement reliability and maintain production quality. Traditional electroluminescence (EL) image-based detection methods often encounter limitations, including inadequate multiscale feature extraction, low sensitivity to minor defects, and excessive model complexity. To address these challenges, we propose the Scale Adaptive Feature Integration Network (SAFINet), an enhanced detection model. SAFINet integrates a complex intercropping feature pyramid network (CIFPN) to strengthen inter-layer feature interactions and employs a Multi-scale Feature Aggregation Attention (MSFAA) mechanism to enhance feature representation across various scales. Furthermore, DualConv modules replace standard convolutions to optimize efficiency, while a Scale-adaptive Weighted Intersection over Union (SA-WIoU) is introduced as a regression loss function aimed at accelerating convergence and improving localization accuracy. Evaluated on the PVEL-AD and ELPV datasets, SAFINet demonstrates significant performance improvements. On the PVEL-AD dataset, the model achieves mean Average Precision scores of mAP_0.5 at 98.8% and mAP_0.5-0.95 at 81.7%, reflecting enhancements of 5.1% and 7.5% over the baseline, respectively. In addition, the inference speed increases from 118 FPS to 178 FPS. In testing on ELPV data, mAP_0.5 improves by 7.2%, while mAP_0.5-0.95 sees an increase of 11.1%, with FPS increasing from 84 to 119. These results confirm the effectiveness of SAFINet in achieving an optimal balance between defect detection accuracy and processing speed, providing high practical value for the diagnosis of PV defects and quality assurance.