Solar Cell Surface Defect Detection Based on Improved YOLO v5

Abstract

A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and perceptual field size; then, the feature extraction capability of the model is enhanced by introducing the ECA-Net attention mechanism; finally, the model network structure is improved and one tiny defect prediction head is added to improve the accuracy of target detection at different scales. To further optimize and improve the YOLO v5 algorithm, this paper uses Mosaic and MixUp fusion data enhancement, K-means++ clustering anchor box algorithm, and CIOU loss function to enhance the model performance. The experimental results show that the improved YOLO v5 algorithm achieves 89.64% mAP for the model trained on the solar cell EL image dataset, which is 7.85% higher than the mAP of the original algorithm, and the speed reaches 36.24 FPS, which can complete the solar cell defect detection task more accurately while meeting the real-time requirements.