Fabric defect detection based on faster R-CNN

Zhoufeng Liu; Xianghui Liu; Chunlei Li; Bicao Li; Baorui Wang

doi:10.1117/12.2303713

10 April 2018 Fabric defect detection based on faster R-CNN

Zhoufeng Liu, Xianghui Liu, Chunlei Li, Bicao Li, Baorui Wang

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150A (2018) https://doi.org/10.1117/12.2303713
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.

Citation Download Citation

Zhoufeng Liu, Xianghui Liu, Chunlei Li, Bicao Li, and Baorui Wang "Fabric defect detection based on faster R-CNN", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150A (10 April 2018); https://doi.org/10.1117/12.2303713

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