计算机应用与软件2024，Vol.41Issue(11)：241-246,6.DOI:10.3969/j.issn.1000-386x.2024.11.034
一种基于级联RCNN的织物瑕疵检测算法
FABRIC DEFECT DETECTION ALGORITHM BASED ON CASCADE RCNN
摘要
Abstract
In order to identify fabric defects and reduce economic losses,aiming at the problems of low detection accuracy and insensitivity to small target detection in some existing network detection methods,this paper proposes a fabric defect algorithm ZS-Cascade RCNN based on cascade RCNN.In the feature extraction stage,deformable convolution was added to preserve the integrity of feature.The anchor frame was adjusted to meet the defect detection requirements of different aspect ratios to improve the detection effect.The cross and parallel ratio equalization sampling was used to equalize positive and negative samples.Experimental results show that the accuracy of ZS-Cascade RCNN algorithm is 4.5 percentage points higher and the average accuracy is 17.8 percentage points higher than that of the original algorithm.The effect of fabric defect detection is obviously improved.关键词
卷积神经网络/深度学习/瑕疵检测/级联检测器Key words
Convolution neural network/Deep learning/Defect detection/Cascade detectors分类
信息技术与安全科学引用本文复制引用
赵玉香,段先华,赵楚..一种基于级联RCNN的织物瑕疵检测算法[J].计算机应用与软件,2024,41(11):241-246,6.基金项目
国家自然科学基金项目(61806087) （61806087）
江苏省研究生创新项目(SJCX20_1475). （SJCX20_1475）
