基于改进Cascade R-CNN的布匹瑕疵检测算法  

Fabric Defect Detection Algorithm Based on Improved Cascade R-CNN

作　　者：白明丽 王明文[1] BAI Mingli;WANG Mingwen(School of Mathematics,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学数学学院,成都611756

出　　处：《计算机科学》2023年第S01期312-317,共6页Computer Science

基　　金：中央高校基本科研业务费专项资金(2682021ZTPY100);四川省科技计划项目(2020YFG0238)。

摘　　要：布匹瑕疵的自动化检测是目前纺织行业面临的一个难点问题。针对当前布匹瑕疵检测算法对尺度和长宽比变化大、小目标众多的样本检测效果并不理想的问题,提出了基于改进Cascade R-CNN网络的布匹瑕疵检测算法。首先,在特征提取网络ResNet-50中融入可变形卷积,自适应地提取更多的瑕疵形状与尺度特征;其次,在特征金字塔网络上采样前引入平衡特征金字塔,缩小特征融合前各特征层之间的语义差距,得到更具表达力的多尺度特征;然后,根据瑕疵尺度与长宽比特点重新设计更适合的初始锚框;最后,采用具有尺度不变性的GIoU Loss作为级联检测器的回归损失,以获取更加精确的瑕疵预测边界框。实验结果表明,相比基于Cascade R-CNN的算法,改进后的Cascade R-CNN算法对布匹瑕疵检测的平均精确率获得了明显提升。Automatic detection of fabric defects is a difficult problem in textile industry.To solve the problem that the current fabric defect detection algorithms have unsatisfactory detection effect on samples with large scale and aspect ratio changes and numerous small targets,a fabric defect detection algorithm based on improved Cascade R-CNN network is proposed.The main improvements are as follows.Firstly,deformable convolution is incorporated into the feature extraction network ResNet-50 to extract more shape and scale features of defects adaptively.Secondly,balanced feature pyramid is introduced in the feature pyramid network before sampling to narrow the semantic gap between each feature layer before feature fusion and get more expressive multi-scale features.Then,more suitable initial anchor boxes are redesigned according to the scale and aspect ratio of defects.Finally,GIoU Loss with scale invariance is used as the regression loss of cascade detector to obtain more accurate defect prediction boundary boxes.Experimental results show that compared with the algorithm based on Cascade R-CNN,the improved Cascade R-CNN algorithm significantly improves the average precision of fabric defect detection.

关 键 词：Cascade R-CNN 布匹瑕疵检测 可变形卷积 平衡特征金字塔 GIoU Loss 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]