局部和全局特征融合的太阳能电池片表面缺陷检测

Surface defect detection of solar cells using local and global feature fusion

太阳能电池片表面缺陷具有类内差异大、类间差异小和背景特征复杂等特点,因此,要实现高精度的太阳能电池片表面缺陷自动检测是一项富有挑战性的任务。针对此问题,该文提出融合局部和全局特征的卷积视觉Transformer网络(CViT-Net),首先采用Ghost聚焦(G-C2F)模块提取电池片缺陷局部特征;然后引进坐标注意力强调缺陷特征并抑制背景特征;最后构建Ghost视觉(G-ViT)模块融合电池片缺陷局部特征和全局特征。同时,针对不同检测精度和模型参数量,分别提供了CViT-Net-S和CViT-Net-L两种网络结构。实验结果表明,与经典MobileVit、MobileNetV3和GhostNet轻量级网络相比,CViT-Net-S对电池片分类准确率分别提升了1.4%、2.3%和1.3%,对电池片检测mAP50分别提升了2.7%、0.3%和0.8%;与ResNet50、RegNet网络相比,CViT-Net-L分类准确率分别提升了0.72%和0.7%,检测mAP50分别提升了3.9%、1.3%;与先进YOLOv6、YOLOv7和YOLOv8检测网络相比,作为骨干网络的CViT-Net-S、CViT-Net-L结构在mAP和mAP50指标上仍保持良好检测效果。结果证明本文算法在太阳能电池片表面缺陷检测领域具有应用价值。

The surface defects of solar cells exhibit significant intra-class differences,minor inter-class differences,and complex background features,making high-precision identification of surface defects a challenging task.This paper proposes a Convolutional-Vision Transformer Network(CViT-Net)that combines local and global features to address this issue.First,a Ghost-Convolution two-fusion(G-C2F)module is used to extract local features of the solar cell panel defects.Then,a coordinate attention mechanism is introduced to emphasize defect features and suppress background features.Finally,a Ghost-Vision Transformer(G-ViT)module is constructed to fuse local and global features of the solar cell panel defects.Meanwhile,CViT-Net-S and CViT-Net-L network structures are provided for low-resource and high-resource environments.Experimental results show that compared to classic lightweight networks such as MobileVit,MobileNetV3,and GhostNet,CViT-Net-S improves the classification accuracy of solar cell panels by 1.4%,2.3%,and 1.3%,respectively,and improves the mAP50 for defect detection by 2.7%,0.3%,and 0.8%respectively.Compared to ResNet50 and RegNet,CViT-Net-L enhances the classification accuracy by 0.72%and 0.7%,respectively,and improves the mAP50 for defect detection by 3.9%and 1.3%,respectively.Compared to advanced YOLOV6,YOLOV7,and YOLOV8 detection networks,CViT-Net-S and CViT-Net-L structures,as backbone networks,still maintain good detection performance in terms of mAP and mAP50 metrics,demonstrating the application value of the proposed algorithm in the field of solar cell panel surface defect detection.