An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2

doi:10.3390/s23041953

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	An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2
	G. Jin; Y. Liu; P. Qin; R. Hong; T. Xu and G. Lu
	2023
发表期刊	Sensors
ISSN	14248220
卷号	23 期号:4
摘要	In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing. © 2023 by the authors.
DOI	10.3390/s23041953
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67572
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	G. Jin,Y. Liu,P. Qin,et al. An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2[J]. Sensors,2023,23(4).
APA	G. Jin,Y. Liu,P. Qin,R. Hong,&T. Xu and G. Lu.(2023).An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2.Sensors,23(4).
MLA	G. Jin,et al."An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2".Sensors 23.4(2023).

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