基于改进YOLOv5的PCB缺陷检测方法

Improved PCB defect detection method based on YOLOv5

印刷电路板作为电子产品不可或缺的重要组成部分,其市场需求量与日俱增,因此制造无缺陷的PCB具有重要意义;针对PCB缺陷检测中待检测的缺陷目标较小且多数检测目标与背景容易混淆导致的误检漏检,改进的算法在原生YOLOv5算法的骨干网络中引入坐标注意力机制,在颈部网络中引入Transformer Encoder并增加一个适用于小目标的高分辨率检测头,并且将选定锚框的交并比算法部分改为更先进的E-IoU。相较于原生YOLOv5算法,根据算法评价指标精确率,召回率和平均检测精度均值的结果,改进后的算法性能有显著提升,其中平均检测精度均值更是高达98.46%,且检测速度也达到了72.4 Hz,可以满足工业现场对PCB缺陷检测的精度要求。

Printed Circuit board is an indispensable part of electronic products,and its market demand is increasing day by day.Therefore,it is of great significance to manufacture PCB without defects.In the PCB defect detection,the defect targets to be detected are small and most of the detection targets are easily confused with the background,so the improved algorithm introduces the Coordinate Attention mechanism into the backbone network of the native YOLOv5 algorithm.A Transformer Encoder was introduced into the neck network and a high-resolution detection head suitable for small targets was added.The Intersection over Union algorithm of selected anchor frames was changed to a more advanced E-IoU.Compared with the original YOLOv5algorithm,the performance of the improved algorithm is significantly improved according to the results of Precision,recall and mean Average Precision of the algorithm evaluation index,and the mean Average Precision is 98.46%.It can meet the precision requirement of PCB defect detection in industrial field.