基于改进YOLOv3的印刷电路板缺陷检测算法

Improved YOLOv3-based defect detection algorithm for printed circuit board

针对现有基于深度学习的印刷电路板(PCB)缺陷检测算法无法同时满足精度和效率要求的问题,提出基于YOLOv3改进的AT-YOLO算法来检测PCB缺陷.将主干网络替换为ResNeSt50,提高特征提取能力,减少参数量.引入SPP模块,融合不同感受野的特征,丰富了特征的表达能力.改进PANet结构替换FPN,插入SE模块提升有效特征图的表达能力,增加1组高分辨率特征图的输入输出,提升对小目标物体的敏感程度,检测尺度由3个增加到4个.使用K-means算法重新聚类生成锚框尺寸,提高了模型的目标检测精度.实验证明,AT-YOLO算法在PCB缺陷检测数据集上的精度均值AP_(0.5)达到98.42%,参数量为3.523×10^(7),平均检测速度为36帧/s,满足精度和效率的要求.

An AT-YOLO algorithm based on improved YOLOv3 was proposed aiming at the problem that the existing deep learning-based defect detection algorithm for printed circuit boards(PCB)could not meet the accuracy and efficiency requirements at the same time.Feature extraction capabilities were improved and the number of parameters was reduced by replacing the backbone with ResNeSt50.SPP module was added to integrate the features of different receptive fields and enrich the ability of feature representation.The PANet structure was improved to replace FPN,and the SE module was inserted to enhance the expression capability of effective feature maps.A set of high-resolution feature maps were added to the input and output in order to improve the sensitivity to small target objects,and the detection scale was increased from three to four.K-means algorithm was re-used to generate sizes of anchors in order to improve the accuracy of object detection.The experimental results showed that the AT-YOLO algorithm had an AP_(0.5) value of 98.42%,the number of parameters was 3.523×10^(7),and the average detection speed was 36 frame per second on the PCB defect detection dataset,which met the requirements of accuracy and efficiency.