Fabric Defect Detection Based on Improved Cascade R-CNN
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.
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