DENG Shishuang1, DI Lan1, LIANG Jiuzhen2, JIANG Daihong3
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164; 3. School of Information Engineering, Xuzhou University of Technology, Xuzhou 221000
Abstract：To solve the problems of high cost, low precision and slow speed of defect detection in textile production process, a textile defect detection model combining attention mechanism and adaptive memory fusion network is proposed. Firstly, the improved attention module is introduced into the YOLOv5 backbone network to build a SCNet feature extraction network and improve the ability to extract textile defect features. Then, an adaptive memory feature fusion network is proposed to enhance the transfer of shallow localization information and effectively mitigate the confounding effect generated during feature fusion. Thus, the feature scale invariance is improved while feature information in the backbone network is incorporated into the feature fusion layer. Finally, the control distance intersection over union loss function is introduced into the proposed model to increase the detection accuracy. Experiments on ZJU-Leaper and Tianchi textile defect datasets show that the proposed model generates higher detection accuracy and speed.
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2022, Vol. 35 
