棉纺织技术2023，Vol.51Issue(12)：26-33,8.
基于弱监督SOD网络的网状白坯织物缺陷检测方法
Defect detection method of mesh white fabric based on weakly supervised SOD network
摘要
Abstract
Aiming at the problems of low detection efficiency caused by complex background and unclear boundary in the current defect detection process of white mesh fabric,an end-to-end method based on weakly supervised SOD network was proposed to realize defect detection of white mesh fabric.Firstly,the single-round end-to-end training of weakly supervised SOD was realized by scribble annotation,which deepened the number of network layers by maximizing pooling and reducing information loss during training.Secondly,the local significant coherence loss and partial cross entropy loss were proposed to solve the problem that sketch labels could not provide detailed information,and the significant structural consistency loss was proposed to improve the adaptability and generalization ability of the model.Finally,a fusion module(CAM)was used to synthesize multi-level features to obtain defect detection results,a boundary refinement module was introduced to improve the accuracy of boundary positioning and make the detected defect significance map clearer.The performance of the algorithm was verified by using the defect images collected by TILDA data set and BASLER industrial camera.The experiments showed that the precision of the proposed algorithm was reached 92.25%,the recall was reached 93.55%.It is considered that the improved weak-supervised SOD network model has high quality and good robustness in the detection of mesh white fabric.关键词
弱监督/显著性检测/图像处理/网状白坯织物/缺陷检测Key words
weak supervision/significant detection/image processing/mesh white fabric/defect detection分类
计算机与自动化引用本文复制引用
刘秀平,王柯欣,冯国栋,闫焕营..基于弱监督SOD网络的网状白坯织物缺陷检测方法[J].棉纺织技术,2023,51(12):26-33,8.基金项目
陕西省科技厅工业领域一般项目(2018GY-173) （2018GY-173）
西安市科技局项目(GXYD7.5) （GXYD7.5）
