CASPPNet: a chained atrous spatial pyramid pooling network for steel defect detection. (1st August 2022)

• Record Type: Journal Article
• Authors: Zheng, Zhouzhou; Hu, Yaohua; Zhang, Yan; Yang, Huanbo; Qiao, Yichen; Qu, Zhanghao; Huang, Yuxiang
• Is Part Of: Measurement science & technology. Volume 33:Number 8(2022)
• Publication Date: 2022-08-01
• Subjects: steel defect detection -- chained atrous spatial pyramid pooling -- global attention feature fusion -- residual boundary refinement -- semantic segmentation; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac68d2
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Automatic machine vision-based defect detection has been successfully applied to many industrial visual inspection applications. However, automatic steel surface defect detection is still a challenging task due to diverse defect categories, low-contrast between defect and complex texture background. To address these challenges, a chained atrous spatial pyramid pooling network (CASPPNet) is proposed for steel surface defect detection. In CASPPNet, chained atrous spatial pyramid pooling is designed to enlarge receptive field and obtain enrich semantic information. An improved global attention feature fusion module is introduced to achieve feature interaction and salience. Moreover, residual boundary refinement block is introduced to get more complete defect boundary. Comparative experimental results verify that our method is superior to the state-of-the-art segmentation methods on public accessible SD-saliency-900 datasets and can meet the requirement of real-time online detection (the detection efficiency is at over 47 FPS on a single GPU).