Defect Detection of Photovoltaic Cells Based on an Improved YOLOv8
Zhihui Li, Liqiang Wang
Abstract
Currently, defect detection in photovoltaic (PV) cells faces challenges such as limited training data, data imbalance, and high background complexity, which can result in both false positives and false negatives during the detection process. To address these challenges, a defect detection network based on an improved YOLOv8 model is proposed. Firstly, to tackle the data imbalance problem, five data augmentation techniques—Mosaic, Mixup, HSV transformation, scale transformation, and flip—are applied to improve the model’s generalization ability and reduce the risk of overfitting. Secondly, SPD-Conv is used instead of Conv in the backbone network, enabling the model to better detect small objects and defects in low-resolution images, thereby enhancing its performance and robustness in complex backgrounds. Next, the GAM attention mechanism is applied in the detection head to strengthen global channel interactions, reduce information dispersion, and enhance global dependencies, thereby improving network performance. Lastly, the CIoU loss function in YOLOv8 is replaced with the Focal-EIoU loss function, which accelerates model convergence and improves bbox regression accuracy. Experimental results show that the optimized model achieves a mAP of 86.6% on the augmented EL2021 dataset, representing a 5.1% improvement over the original YOLOv8 model, which has 11.24 × 10^6 parameters. The improved algorithm outperforms other widely used methods in photovoltaic cell defect detection.