多尺度YOLOv5的太阳能电池缺陷检测

Multi-scale YOLOv5 for solar cell defect detection

为了实现电致发光(Electroluminescent,EL)条件下太阳能电池的高精度裂纹和碎片缺陷检测,将多尺度YOLOv5(You Only Look Once version 5)模型用于真实工况下的太阳能电池缺陷检测。首先,提出一种融合可变形卷积(Deformable Convolutional Networks Version 2,DCNv2)和坐标注意力(Coordinate Attention,CA)的改进特征提取网络,拓宽小目标缺陷的感受野,有效增强小尺度缺陷特征的提取。其次,提出一种名为CA-PANet的改进路径聚合网络(Path Aggregation Network,PANet),将CA与跨层级联整合在路径增强结构中,实现浅层特征的复用,使深层特征和浅层特征结合,增强不同尺度缺陷的特征融合,提高缺陷的特征表达能力,提升缺陷检测框的准确度。轻量级CA的计算成本低,保证了模型的实时性。实验结果表明,融合DCNv2与CA注意力的YOLOv5模型平均精度均值(Mean Average Precision,mAP)值可达95.4%,较YOLOv5模型提高3%,较YOLOX模型提高1.4%。每秒帧数(Frames Per Second,FPS)可达51,满足工业实时性需求。对比其它算法,改进YOLOv5模型能精确检测到太阳能电池的微裂纹和碎片缺陷,能满足光伏电站真实工况下的实时高精度缺陷检测需求。

Herein,to realize high-precision crack and break defect detection in solar cells under electroluminescent(EL)conditions,the multi-scale You Only Look Once version 5(YOLOv5)model is used for solar-cell defect detection under real industrial conditions.First,an improved feature-extraction network combining deformable convolution version 2(DCNv2)and coordinate attention(CA)is proposed to widen the receptive field of small target defects and enhance the extraction of small-scale defect features.Second,an improved path aggregation network(PANet),called CA-PANet,is proposed for integrating the CA and cross-layer cascade in a path aggregation network to multiplex shallow features.Notably,the CAPANet combines deep and shallow features to enhance the feature fusion of defects at different scales,improve the feature representation of defects,and increase the defect detection accuracy.The low computational cost of the lightweight CA ensures the real-time performance of the model.Experimental results indicate that the mean average precision(mAP)of the YOLOv5 model combining DCNv2 and CA can reach 95.4%,which is 3%higher than that of the YOLOv5 model and 1.4%higher than that of the YOLOX model.The improved YOLOv5 model can achieve a frame rate of up to 51 frames per second(FPS),meeting industrial real-time requirements.Compared with other algorithms,the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells,satisfying the demand for real-time,high-precision defect detection under industrial conditions in photovoltaic power plants.