计算机工程与应用2025，Vol.61Issue(6)：128-140,13.DOI:10.3778/j.issn.1002-8331.2401-0382
基于YOLOv8-S的偏光片表面缺陷检测算法
Polarizer Surface Defect Detection Algorithm Based on YOLOv8-S
摘要
Abstract
As the polarizer market continues to expand,the application is more and more extensive,the production requirements for polarizer are also more and more stringent.Aiming at the problems of complex defect morphology,small-size defects detection false and missed in polarizer surface defect detection,this paper proposes an improved algorithm based on YOLOv8-S polarizer surface defect detection.DCNv3 is used to replace the ordinary convolution in the C2f module of the backbone network,and at the same time,combining with the EMA,the DEC2f feature extraction module is constructed,which improves the feature extraction capability of the backbone network for complex defects.Lightweight cross-scale feature refinement fusion module(LCFRFM)is constructed based on the feature refinement module to improve the channel purification capability and reduce the number of parameters,and effectively cross-scale fusion of shallow features in the backbone network.The ConvMixer Layer is introduced to construct the CMC2f prediction head,and the larger prediction field of view brings stronger small-size defect detection capability.SIoU is used to replace CIoU as the bounding box regression loss function,and AdamW is used to replace SGD as the optimizer during network training to improve the detection accuracy and training convergence speed.The experimental results show that the proposed algo-rithm improves 2.4 and 2.9 percentage points on mAP50 and mAP50:95,respectively,compared to YOLOv8-S,which proves the effectiveness of the proposed algorithm.关键词
偏光片表面/缺陷检测/特征提取/跨尺度特征融合/YOLOv8Key words
polarizer surface/defect detection/feature extraction/cross-scale feature fusion/YOLOv8分类
信息技术与安全科学引用本文复制引用
盛威,周永霞,陈俊杰,赵平..基于YOLOv8-S的偏光片表面缺陷检测算法[J].计算机工程与应用,2025,61(6):128-140,13.基金项目
浙江省"领雁"研发计划项目(2024C01107). （2024C01107）
