Optimized YOLO based model for photovoltaic defect detection in electroluminescence images

Ensuring the reliability of photovoltaic (PV) systems requires efficient defect detection to maintain optimal energy production. Deep learning-based object detection models have demonstrated remarkable performance in automating this process. In this study, PV-YOLOv12n is introduced as an optimized variant of YOLOv12n, tailored for defect detection in electroluminescence (EL) images of PV panels. The modifications incorporate an A2C2f module at the P5 scale (1024, True), which enhances feature extraction by prioritizing critical defect regions. This improvement significantly boosts recall and precision for detecting large cracks, significant dislocations, and widespread material inconsistencies. Experimental results on the PVEL-AD and Roboflow datasets demonstrate superior detection performance. PV-YOLOv12n achieves a mAP@50 of 0.91 on both datasets, surpassing the baseline YOLOv12n (mAP@50 of 0.90 and 0.88 for PVEL-AD and Roboflow, respectively). Additionally, mAP@50-95 increases to 0.58 on PVEL-AD and 0.75 on Roboflow, highlighting improved generalization. Despite these improvements, inference speed remains efficient at 4.24 ms for PVEL-AD and 4.43 ms for Roboflow, ensuring suitability for real-time applications. These results validate the effectiveness of PV-YOLOv12n in detecting critical PV panel defects, supporting its deployment in large-scale solar farm inspections.