CASI-Net: A Novel and Effect Steel Surface Defect Classification Method Based on Coordinate Attention and Self-Interaction Mechanism

Zhong Li, Chen Wu, Qi Han, Mingyang Hou, Guorong Chen and Tengfei Weng
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Zhong Li: College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Chen Wu: College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Qi Han: College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Mingyang Hou: College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Guorong Chen: College of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
Tengfei Weng: College of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

Mathematics, 2022, vol. 10, issue 6, 1-14

Abstract: 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.

Keywords: hot-rolled steel strip; defect classification; convolutional neural network; attention mechanism; visual interaction mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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