改进Faster R-CNN的微型扁平电机FPC表面焊点缺陷检测

Defect detection of FPC surface welding spot defects of miniature flat motor based on Faster R-CNN

目前微型扁平电机制造厂仍采用人工观察法对电机FPC板焊点的焊接质量进行检测,其检测准确率低、速度慢。针对这一问题,提出了一种基于改进Faster R-CNN的缺陷分类检测方法。首先通过多尺度特征融合网络对VGG16的最后两层网络进行融合后,代替原Faster R-CNN中区域候选网络的输入特征图,然后从3个不同深度的多尺度特征融合算法比较改进后网络的准确率、召回率和分数。试验结果表明,改进后的两层多尺度融合特征图代入模型,其缺陷分类检测准确率均值为91.89%,比传统模型增加了7.72%;与其他两种模型相比,改进后的模型分类检测准确率和精度是最高的。

At present,micro flat motor manufacturers still use manual observation of motor FPC surface welding quality for classification,its detection accuracy is low,slow speed.To solve this problem,a defect classification detection method based on improved Faster R-CNN was proposed.Firstly,the last two layers of VGG16 are fused by multi-scale feature fusion network to replace the input feature graph of the regional proposal network in the original Faster R-CNN.Then,the accuracy,recall rate and score of the network are compared from three multi-scale feature fusion algorithms with different depths.The experimental results show that the average accuracy of defect classification detection of the improved two-layer multi-scale fusion feature map is 91.89%,7.72%higher than that of the traditional model.Compared with the other two models,the improved model has the highest classification detection accuracy and precision.