Abstract
Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.
Highlights
Fabric defect detection is a necessary quality inspection process, which aims to classify and locate defects in textiles
The proposed PRAN-Net based on Faster R-CNN in this paper was compared with RetinaNet, Mask R-CNN and Faster R-CNN with Guided Anchoring (GA-Faster R-CNN) on two fabric datasets, and all compared algorithms used ResNet-101-Feature Pyramid Network (FPN) [25] as backbone
The detection results were evaluated by ACC, Average Recall (AR), mean Average Precision (mAP), intersection over union (IoU) and Frame Per Second (FPS)
Summary
Fabric defect detection is a necessary quality inspection process, which aims to classify and locate defects in textiles. Sci. 2020, 10, 8434 ratios, such as people, airplanes, etc Many methods, such as GAN [19], deep convolutional neural network [20] and YOLO v3 [21], are effective in detecting fabric defects, but cannot detect exceedingly small and extreme aspect ratios fabric defects well. The Mask RCNN [14] method improves the tiny defects detection accuracy, but the runtime of detection is much longer These methods improve the detection accuracy of tiny defects, its location accuracy for extreme shape defects is still unsatisfied, because the preset fixed size anchors cannot accurately match extreme aspect ratios fabric defects in images. We proposed a trick to generate sparse priori anchors, which can match extreme aspect ratio defects well and remove a large number of redundant anchors in order to improve the accuracy and efficiency of the fabric defect detection. A classification network is used to classify and refine the position of the fabric defects
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