基于改进YOLOv5s的太阳能电池缺陷检测算法

Improved Solar Cell Defect Detection Algorithm Based on YOLOv5s

太阳能电池生产制造的过程中,由于制造工艺的不完善和人为操作失误等原因可能导致太阳能电池片出现破损、裂缝、断栅和硅材料缺失等类型的缺陷。为了提高太阳能电池缺陷检测准确率,本文提出一种基于YOLOv5s算法的太阳能电池缺陷检测算法YOLOv5s-CG。在主干网络和特征融合层不同位置引入卷积注意力机制(CBAM),主干网络的注意力机制关注全局信息,特征融合层的注意力机制关注局部信息,同时在空间和通道2种维度上进行特征增强,并用GIOU损失函数评估检测效果。使用重新标注的公开太阳能电池数据集对提出的算法进行实验验证,实验结果表明,YOLOv5s-CG算法的全类平均精度(mAP)达到了75.1%,与YOLOv5s算法比较,各种类型的缺陷检测精度都有所提升,其中裂缝和硅材料缺失的精度分别提升了0.036、0.033,全类平均精度(mAP)提高了0.026;与主流的目标检测算法SSD相比,全类平均精度(mAP)提升了0.123。本文算法能够更加高效地检测太阳能电池的缺陷,为实际生产提供更好的检测算法。

In the process of manufacturing solar cells,due to the imperfect manufacturing processes and operational failure of hu⁃mans,the defects,such as broken cell,crack,finger failure and silicon material missing might be found in the solar cells.A so⁃lar cell defect detection model based on YOLOv5s,namely YOLOv5s_CG,is proposed to improve the precision of the solar cell defects detection.The algorithm introduces convolutional attention mechanism(CBAM)blocks in different positions of the back⁃bone network and feature fusion layer.The attention mechanism of the backbone network focuses on the global information,and the attention mechanism of the feature fusion layer focuses on the local information.At the same time,it enhances the features in both spatial and channel dimensions and uses the GIOU loss function to evaluate the detection effect of the algorithm.The pro⁃posed method is tested on the open source solar cell dataset which is re-labeled by the authors.The experimental results show that the overall mean average precision(mAP)of the YOLOv5s-CG algorithm reaches 75.1%.Compared with the algorithm of YOLOv5s,various types of defect detection accuracy have been improved,among which the accuracy of crack and silicon mate⁃rial missing has increased by 0.036 and 0.033 respectively,and the average accuracy(mAP)of all classes has increased by 0.026.Compared with the mainstream target detection algorithm of SSD,the overall mean average precision(mAP)has im⁃proved by 0.123.The algorithm can accurately detect the defects of solar cells,which could provide a better defects detection al⁃gorithm for real solar cell production.