计算机与数字工程2024，Vol.52Issue(5)：1557-1562,1568,7.DOI:10.3969/j.issn.1672-9722.2024.05.051
基于改进级联卷积神经网络的织物疵点检测
Fabric Defect Detection Based on Improved Cascade R-CNN
摘要
Abstract
The work aims to propose an end-to-end improved algorithm for fabric defect detection in order to solve the prob-lems in the current cloth detection algorithm including few samples,low defect detection accuracy and poor positioning accuracy.Aiming at the problem of lacking samples and imbalance of classes in public data sets,offline and online data augmentation meth-ods are adopted.In addition to basic data augmentation methods,copy-paste and mixup are also introduced to expand and grow sam-ples.Aiming at the poor accuracy features extracted by the feature extraction algorithm,the feature pyramid network is improved by adding deformable convolution,recursive feature pyramid,switchable atrous convolution,global context to enlarge the receptive field and enhance semantic information.The experimental results verify the effectiveness of the algorithm.This algorithm can defect 9 kinds of cloth defects,the accuracy of detecting whether the fabric is defective is above 97%,the average detection accuracy of de-fect location is 56.7%and the efficiency of sample detecting is 2.4 FPS on TIANCHI-XUELANGAI dataset.Compared with the ba-sic model,the positioning accuracy has been improved by more than 10%and the algorithm meeting industrial production needs.关键词
织物疵点检测/级联卷积神经网络/数据增广/递归特征金字塔/可切换空洞卷积Key words
fabric target detection/Cascade R-CNN/data augmentation/recursive feature pyramid/switchable atrous con-volution分类
轻工纺织引用本文复制引用
李小庆,张俊杰,杜小勤,梁晶,袁桦..基于改进级联卷积神经网络的织物疵点检测[J].计算机与数字工程,2024,52(5):1557-1562,1568,7.基金项目
湖北省普通高校人文社会科学重点研究基地项目(编号:2021HFG007)资助. （编号:2021HFG007）
