文献类型：期刊文献

英文题名：Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization

作者：Chen, Mengqi[1,2];Yu, Lingjie[1,2];Zhi, Chao[1,2];Sun, Runjun[1,2];Zhu, Shuangwu[1,2];Gao, Zhongyuan[1,2];Ke, Zhenxia[1,2];Zhu, Mengqiu[1,2];Zhang, Yuming[3]

机构：[1]Xian Polytech Univ, Sch Text Sci & Engn, Xian 710048, Shaanxi, Peoples R China;[2]Xian Polytech Univ, State Key Lab Intelligent Text Mat & Prod, Xian 710048, Shaanxi, Peoples R China;[3]Shaoxing Univ, Yuanpei Coll, Shaoxing 312000, Zhejiang, Peoples R China

年份：2022

卷号：134

外文期刊名：COMPUTERS IN INDUSTRY

收录：SCI-EXPANDED(收录号：WOS:000820899900002)、、EI(收录号：20214511117771)、Scopus(收录号：2-s2.0-85118509369)、WOS

基金：This work was supported by the financial support from the National Natural Science Foundation of China (Grant No. 51903199), Outstanding Young Talents Support Plan of Shaanxi Universities (2020), Natural Science Basic Research Program of Shaanxi (No. 2019JQ-182 and 2018JQ5214), Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 18JS039), Science and Technology Guiding Project of China National Textile and Apparel Council (No. 2020044 and No. 2020046).

语种：英文

外文关键词：Fabric defect detection; Faster R-CNN; Gabor filter; Genetic algorithm

外文摘要：Fabric defect detection plays a crucial role in fabric inspection and quality control. Convolutional neural networks (CNNs)-based model has been proved successful in various defect inspection applications. However, the sophisticated background texture is still a challenging task for fabric defect detection. To address the texture interference problem, taking advantage of Gabor filter in frequency analysis, we improved the Faster Region-based Convolutional Neural Network (Faster R-CNN) model by embedding Gabor kernels into Faster R-CNN, termed the Genetic Algorithm Gabor Faster R-CNN (Faster GG R-CNN); in addition, a two-stage training method based on Genetic Algorithm (GA) and back-propagation was designed to train the new Faster GG R-CNN model; finally, extensive experimental validations were conducted to evaluate the proposed model. The experimental results show that the proposed Faster GG R-CNN model outperforms the typical Faster R-CNN model in terms of accuracy. The proposed method' mean average precision (mAP) is 94.57%, compared to 78.98% with the Faster R-CNN. (C) 2021 Elsevier B.V. All rights reserved.