Affiliation:
1. School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
3. Guangdong Provincial Key Laboratory of Modern Control Technology, Guangzhou 510070, China
Abstract
In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.
Funder
Jihua Laboratory Foundation of the Guangdong Province Laboratory of China
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
Guangdong Province Key Areas R&D Program
Guangzhou Key R&D Program
International Science and Technology Cooperation Project of Huangpu
GDAS’ Project of Science and Technology Development
Subject
General Materials Science
Cited by
4 articles.
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