SEM-YOLO: A Small Target Defect Detection Model for Photovoltaic Modules

Defect detection is key to extending the lifetime of PV modules. However, existing methods still face significant challenges in detecting small and ambiguous targets. To this end, this paper proposes a PV module defect detection model, SEM-YOLO, based on YOLOv8. The model improves the performance through the following improvements: first, the SPD-Conv module is introduced to replace the traditional convolution in the backbone and neck sections to reduce the information loss caused by excessive down-sampling, thus enhancing the detection of small targets. Second, the neck section C2f-EMA module is introduced, in which the efficient multiscale attention module (EMA) enhances feature extraction by redistributing weights and prioritizing relevant features to improve the perception and recognition of small target defects (hot spots). Finally, we add a small target detection layer and increase the MultiSEAM detection header, so that the model can capture and detect small targets more efficiently at the output stage. The experimental results show that the mAP of the improved model reaches 93.8%, among which the mAP of small target defects reaches 83%, which is an improvement of 2.23% and 7.62% compared with YOLOv8. In addition, compared with the mainstream models (RT-DETR, YOLOv9s, YOLOv10n, and YOLOv11), the detection accuracies in terms of overall and small-target defects are significantly improved, which further validates the effectiveness of the model.