针对印刷电路板(Printed Circuit Board,PCB)表面缺陷检测任务中模型体积和参数量较大的问题,提出了一种基于通道剪枝的轻量级YOLOv8n网络PCB缺陷检测算法。为有效提升对PCB小目标缺陷的特征提取能力,采用RepViT作为特征提取网络;为提...针对印刷电路板(Printed Circuit Board,PCB)表面缺陷检测任务中模型体积和参数量较大的问题,提出了一种基于通道剪枝的轻量级YOLOv8n网络PCB缺陷检测算法。为有效提升对PCB小目标缺陷的特征提取能力,采用RepViT作为特征提取网络;为提升网络对小目标的关注度,减少神经网络推理过程中的梯度信息重复,将颈部网络的卷积模块替换为Rep-Net with Cross-Stage Partial CSP and ELAN(RepNCSPELAN4);为降低缺陷重叠时检测框失真现象,在预测部分使用Focaler-MPDIoU替换完全交并比(Complete Intersection over Union,CIoU);利用层自适应幅度分数剪枝(Layer Adaptive Magnitude based Pruning,LAMP)方法对融合改进方法的模型进行修剪,去除模型中冗余的梯度信息和权重,减少参数量和浮点运算量,压缩模型体积。实验结果表明,在PCB公开数据集中,经过LAMP之后,该算法相较于YOLOv8n,参数量下降60.8%,模型体积减小50.8%,计算量下降48.8%,平均精度均值(mean Average Precision,mAP)提高3.8%。在提高精度的同时,计算量、参数量和模型体积都低于原模型,满足在低配置设备下的使用需求。展开更多
To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and tr...To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.展开更多
文摘针对印刷电路板(Printed Circuit Board,PCB)表面缺陷检测任务中模型体积和参数量较大的问题,提出了一种基于通道剪枝的轻量级YOLOv8n网络PCB缺陷检测算法。为有效提升对PCB小目标缺陷的特征提取能力,采用RepViT作为特征提取网络;为提升网络对小目标的关注度,减少神经网络推理过程中的梯度信息重复,将颈部网络的卷积模块替换为Rep-Net with Cross-Stage Partial CSP and ELAN(RepNCSPELAN4);为降低缺陷重叠时检测框失真现象,在预测部分使用Focaler-MPDIoU替换完全交并比(Complete Intersection over Union,CIoU);利用层自适应幅度分数剪枝(Layer Adaptive Magnitude based Pruning,LAMP)方法对融合改进方法的模型进行修剪,去除模型中冗余的梯度信息和权重,减少参数量和浮点运算量,压缩模型体积。实验结果表明,在PCB公开数据集中,经过LAMP之后,该算法相较于YOLOv8n,参数量下降60.8%,模型体积减小50.8%,计算量下降48.8%,平均精度均值(mean Average Precision,mAP)提高3.8%。在提高精度的同时,计算量、参数量和模型体积都低于原模型,满足在低配置设备下的使用需求。
文摘To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.
