基于改进Cascade RCNN算法的电路板缺焊检测  

Missing solder detection of circuit board based on improved Cascade RCNN algorithm

作　　者：李玮 高林[1] 赵杰 Li Wei;Gao Lin;Zhao Jie(School of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,青岛266061

出　　处：《电子测量技术》2022年第6期112-118,共7页Electronic Measurement Technology

基　　金：山东省自然科学基金(ZR2021MFZ23)项目资助。

摘　　要：现阶段工业生产线,多类型电路板焊接无法采用自动化器械。针对贴片元器件人工焊接存在缺焊的现象,为了减少工厂因返工而造成的人力和物力损失,提出通过机器视觉技术对贴片元器件焊接情况进行自动检测。使用改进的ResNet-FPN结构,将浅层特征信息进行多尺度通道融合,从而增加了微小目标和遮挡目标特征信息的丰富性,减少了训练参数,加快了网络训练的前向速度;通过引入焦点损失(Focal loss),平衡了分类样本数量,减小了损失值。实验结果表明,改进的Cascade RCNN算法训练速度稍快于原始模型,召回率小幅度提高,平均精度均值(mAP)达到90.9%,比原始模型提高了2.2%,取得了更好的检测效果。At this stage of industrial production line, many types of circuit board soldering can not be automated instruments. In order to reduce the loss of manpower and material resources due to rework in factories for the phenomenon of missing solder in manual soldering of SMD components, automatic detection of soldering of SMD components by machine vision technology is proposed. Using the improved ResNet-FPN structure, the shallow feature information is fused with multi-scale channels, thus increasing the richness of feature information of tiny and occluded targets, reducing the training parameters, and speeding up the forward speed of network training. The number of classification samples is balanced and the loss value is reduced by introducing the Focal loss(FL). The experimental results show that the improved Cascade RCNN algorithm trains slightly faster than the original model, with a small increase in recall and an average mean accuracy(mAP) of 90.9%, which is 2.2% higher than the original model, and achieves better detection results.

关 键 词：贴片元器件 机器视觉技术 焊接 ResNet-FPN 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]