Fabric defect detection and classification via deep learning-based improved Mask RCNN

Fabric defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. It is challenging to detect fabric defects automatically because of the complexity of images and the variety of patterns in textiles. This study presented a deep learning-based IM-RCNN for sequentially identifying image defects in patterned fabrics. Firstly, the images are gathered from the HKBU database and these images are denoised using a contrast-limited adaptive histogram equalization filter to eliminate the noise artifacts. Then, the Sobel edge detection algorithm is utilized to extract pertinent attention features from the pre-processed images. Lastly, the proposed improved Mask RCNN (IM-RCNN) is used for classifying defected fabric into six classes, namely Stain, Hole, Carrying, Knot, Broken end, and Netting multiple, based on the segmented region of the fabric. The dataset that can be evaluated using the true-positive rate and false-positive rate parameters yields a higher accuracy of 0.978 for the proposed improved Mask RCNN. The proposed IM-RCNN improves the overall accuracy of 6.45%, 1.66%, 4.70%, and 3.86% better than MobileNet-2, U-Net, LeNet-5, and DenseNet, respectively.