现代信息科技2024，Vol.8Issue(5)：89-93,101,6.DOI:10.19850/j.cnki.2096-4706.2024.05.020
基于PCS-YOLOv5轻量化模型的布匹外观缺陷检测方法
A Fabric Appearance Defect Detection Method Based on PCS-YOLOv5 Lightweight Model
刘伟鑫 1林邦演 1张彬腾 1姚其广 1徐成烨1
作者信息
- 1. 东莞市新一代人工智能产业技术研究院,广东 东莞 523867
- 折叠
摘要
Abstract
In response to the problems of large number of parameters,high computational complexity,and slow detection speed when deployed on ordinary industrial computers in existing fabric appearance defect detection models,this paper proposes a lightweight model PCS-YOLOv5.Firstly,PP—LCNet is used to replace the YOLOv5 backbone network to achieve model lightweight and accelerate inference speed.It introduces the CBAM attention module into the Neck network to suppress interference and focus on important features,thereby improving the accuracy of object detection.It modifies the bounding box regression loss function to SIoU to enhance the accuracy of defect localization.The experimental test results show that compared to the YOLOv5 original model,PCS-YOLOv5 performs better in mAP@0.5 under the condition of basic consistency,the detection speed is increased by 10.2%,the number of parameters is reduced by 56.8%,the computational complexity is reduced by 63%,and the model weight is reduced by 56%,which can meet the requirements of online detection of fabric appearance defects on site.关键词
YOLOv5/轻量化/注意力机制/SioUKey words
YOLOv5/lightweight/Attention Mechanism/SIoU分类
信息技术与安全科学引用本文复制引用
刘伟鑫,林邦演,张彬腾,姚其广,徐成烨..基于PCS-YOLOv5轻量化模型的布匹外观缺陷检测方法[J].现代信息科技,2024,8(5):89-93,101,6.
