Abstract: 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.

Key words: Cascade R-CNN, Fabric defect detection, Deformable convolution, Balanced feature pyramid, GIoU Loss