10 April 2018 Fabric defect detection based on faster R-CNN
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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.
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Zhoufeng Liu, Zhoufeng Liu, Xianghui Liu, Xianghui Liu, Chunlei Li, Chunlei Li, Bicao Li, Bicao Li, Baorui Wang, 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); doi: 10.1117/12.2303713; https://doi.org/10.1117/12.2303713
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