东华大学学报（英文版）2024，Vol.41Issue(4)：416-427,12.DOI:10.19884/j.1672-5220.202404009
基于机器视觉的非织造材料疵点高速检测算法改进
Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision
摘要
Abstract
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved NanoDet-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the NanoDet-Plus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original NanoDet-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.关键词
疵点检测/非织造材料/深度学习/目标检测算法/迁移学习/半精度量化Key words
defect detection/nonwoven materials/deep learning/object detection algorithm/transfer learning/half-precision quantization分类
信息技术与安全科学引用本文复制引用
李成族,位珂晗,赵英博,田雪慧,钱洋,张璐,王荣武..基于机器视觉的非织造材料疵点高速检测算法改进[J].东华大学学报（英文版）,2024,41(4):416-427,12.基金项目
National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605) （Nos.2022YFB4700600 and 2022YFB4700605）
National Natural Science Foundation of China(Nos.61771123 and 62171116) （Nos.61771123 and 62171116）
Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044) （No.CUSF-DH-D-2022044）
