改进YOLOv8的PCB表面缺陷检测算法  

Improved PCB surface defect detection algorithm for YOLOv8

作　　者：吕秀丽[1] 杨昕升 曹志民[1] Lyu Xiuli;Yang Xinsheng;Cao Zhimin(College of Physics and Electronic Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学物理与电子工程学院,大庆163318

出　　处：《电子测量技术》2024年第12期100-108,共9页Electronic Measurement Technology

基　　金：黑龙江省教育科学规划重点课题(GJB1421131);海南省科技专项(ZDYF2022GXJS222)资助。

摘　　要：针对目前PCB表面缺陷检测方法存在复杂度过高、误检、漏检等问题,提出了一种改进YOLOv8的轻量型检测算法。由于YOLOv8主干网络下采样后的特征图存在一定冗余,设计轻量级的多尺度混合卷积(MSMC),并结合C2f模块增强多尺度特征提取能力;在颈部网络中设计改进的双向特征金字塔结构(BiFPN),使用两个跨层连接得到更加丰富的语义信息;使用C2f-Faster模块减少特征融合过程中的运算量;引入CA注意力机制与WIoUv2损失函数,增强对PCB小目标缺陷的定位能力。实验结果表明,相较于YOLOv8n,改进后的算法在PCB数据集上检测精度提高了2.2%,模型参数量和计算量降低了36.7%和18.5%,分别为1.9 M和6.6 G。最终模型大小仅为3.8 MB,为移动终端设备部署提供了新思路。A lightweight detection algorithm based on improving YOLOv8 is proposed to address the issues of high complexity,false alarms,and missed detections in current PCB surface defect detection methods.Due to some redundancy in the feature maps of the YOLOv8 backbone network after downsampling,a lightweight multi-scale mixed convolution(MSMC)is designed.This is combined with the C2f module to enhance the capability of extracting features at different scales.Additionally,an improved Bidirectional Feature Pyramid Network(BiFPN)structure is designed in the neck network,using two cross-layer connections to obtain richer semantic information.The C2f-Faster module is employed to reduce computational complexity during the feature fusion process.Moreover,the introduction of the CA attention mechanism and the WIoUv2 loss function strengthens the ability to locate small defects on PCBs.The experimental results show that the improved algorithm compared to YOLOv8n improves the detection accuracy by 2.2%on the PCB dataset,while the number of model parameters and the computation volume are reduced by 36.7%and 18.5%to 1.9 M and 6.6 G.The final model size is only 3.8 MB,providing a new approach for mobile terminal device deployment.

关 键 词：表面缺陷检测 YOLOv8 轻量化 多尺度特征 小目标缺陷 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN41[自动化与计算机技术—计算机科学与技术]