基于特征残差的色织物瑕疵检测

为解决自动织物瑕疵检测算法中,未知花色织物瑕疵检测困难的问题,提出了一种基于特征残差的色织物瑕疵检测方法.首先使用瑕疵织物图像与模板织物图像的瑕疵残差和正常无标注织物图像进行融合,生成新花色瑕疵织物样本;然后改进特征提取网络采用共享权值方法,对瑕疵织物和模板织物提取特征后计算得到特征残差;最后使用ROIAlign方法将全局上下文信息缩放到和感兴趣区域统一大小后进行特征融合,对融合特征进行瑕疵分类和位置回归.实验针对不包含未知花色和包含未知花色的不同测试集分别进行算法测试实验,结果表明改进后的算法能够较好地消除织物花色对检测结果的影响,在不包含未知花色的测试集中精度得到了较大的提升,在包含未知花色的测试集中,瑕疵检测效果依旧保持不错的精度,相较于改进前的通用算法,最终score分别提升了15.4％和16.2％.

Fabric Defect Detection Based on Feature Residual

To solve the difficulty in detecting the defects in fabrics of unknown styles in the automatic fabric defect detection algorithm, this study proposes a fabric defect detection method based on feature residuals. First, the defect residuals of the defective and template fabric images are fused with that of the normal unlabeled fabric image to generate a new defective fabric sample. Then, the improved feature extraction network uses the shared weight method to extract features from the defective and template fabrics and calculate the feature residuals. Finally, the ROIAlign method is used to mix the global context information and the region of interest for feature fusion. The fused features are subject to defect classification and location return. Experiments are separately conducted on two test sets containing and not containing fabrics of unknown styles. The results show that the improved algorithm can better eliminate the influence of fabric styles on the detection results. The accuracy is greatly improved in the test set that doesnot include unknown styles. In the test set containing unknown styles, defect detection maintains high accuracy. Compared with that of the general algorithm before the improvement, the final scores have increased by 15.4% and 16.2%, respectively.