基于DBBR-YOLO的光伏电池表面缺陷检测

Photovoltaic cell surface defect detection based on DBBR-YOLO

针对光伏电池表面缺陷特征提取困难以及检测的实时性和准确性问题,提出了一种基于DBBR-YOLO的光伏电池表面缺陷检测方法。首先,将多样化分支块(DBB)融入到YOLOv8n中Backbone部分的C2f模块中,引入多样化的特征提取路径,增强特征提取的能力;其次,将模型的Neck部分和Gold-YOLO进行融合,实现对不同层级特征的全局信息聚合和融合,提高了特征图之间的信息交互效率,增强了模型的特征表达能力;最后,引入SimAM注意力机制提高了特征的表达能力,以增强模型对微小缺陷或小目标的检测能力。实验选取5种光伏电池表面缺陷类型进行验证,结果表明:改进后的DBBR-YOLO模型mAP50值达到93.1%,相较于YOLOv8n提升了3.7%,FPS值达到了158.0,该模型在精度和速度方面的性能可以满足实时性、准确性的要求,能够应对光伏电池表面缺陷检测的实际应用场景。

A method for detecting surface defects of photovoltaic(PV)cells based on DBBR-YOLO was proposed to address the difficulties in defect feature extraction and the issues of real-time detection and accuracy.Firstly,a diverse branch block(DBB)was incorporated into the C2f module of the YOLOv8n Backbone section to introduce diversified feature extraction paths,enhancing the capability of feature extraction.Secondly,the Neck section of the model was fused with Gold-YOLO to achieve global information aggregation and feature fusion at different hierarchical levels,improving the efficiency of information interaction between feature maps and enhancing the feature expression capability of the model.Finally,the SimAM attention mechanism was introduced to improve the feature expression capability,thereby enhancing the model’s ability to detect small defects or targets.Experiments conducted on five types of PV cell surface defects demonstrated that the improved DBBR-YOLO model achieved an mAP50 value of 93.1%,a 3.7%improvement over YOLOv8n,with an FPS value of 158.0.The performance of the model in terms of accuracy and speed can meet the requirements for real-time detection and accuracy,making it suitable for practical application scenarios of detecting PV cell surface defects.