期刊
MATHEMATICS
卷 10, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/math10060963
关键词
hot-rolled steel strip; defect classification; convolutional neural network; attention mechanism; visual interaction mechanism
类别
资金
- West Light Foundation of the Chinese Academy of Science
- Research Foundation of The Natural Foundation of Chongqing City [cstc2021jcyj-msxmX0146]
- Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJZD-K201901504, KJQN201901537]
- Ministry of Education [19YJCZH047]
- Scientific and Technological Research Program of Luzhou City [2021-JYJ-92]
- China Scholarship Council
In this paper, a lightweight network CASI-Net based on coordinate attention and self-interaction is proposed for automatic identification of hot-rolled steel strip surface defects. Experimental results show that CASI-Net achieves accurate defect classification with reduced computation.
The surface defects of a hot-rolled strip will adversely affect the appearance and quality of industrial products. Therefore, the timely identification of hot-rolled strip surface defects is of great significance. In order to improve the efficiency and accuracy of surface defect detection, a lightweight network based on coordinate attention and self-interaction (CASI-Net), which integrates channel domain, spatial information, and a self-interaction module, is proposed to automatically identify six kinds of hot-rolled steel strip surface defects. In this paper, we use coordinate attention to embed location information into channel attention, which enables the CASI-Net to locate the region of defects more accurately, thus contributing to better recognition and classification. In addition, features are converted into aggregation features from the horizontal and vertical direction attention. Furthermore, a self-interaction module is proposed to interactively fuse the extracted feature information to improve the classification accuracy. The experimental results show that CASI-Net can achieve accurate defect classification with reduced parameters and computation.
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