Q. Luo et al., "Waterdrop Removal From Hot-Rolled Steel Strip Surfaces Based on Progressive Recurrent Generative Adversarial Networks," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, 2021, Art no. 5017011, doi: 10.1109/TIM.2021.3098825
Waterdrop removal from hot-rolled steel strip surfaces based on progressive recurrent generative adversarial networks
|Author:||Luo, Qiwu1; Liu, Kexin1; Su, Jiaojiao1;|
1School of Automation, Central South University, Changsha 410083, China
2College of System Engineering, National University of Defense Technology, Changsha 410073, China
3Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021110453713
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-11-04
Automated visual inspection (AVI) instrument of surface defects for hot-rolled steel strips is conventionally installed closely before the terminal crimping machine, where the adjacent upstream process is laminar spray cooling. Waterdrops, spreading more or less over the steel strip surface, often trigger false alarms, which is a quite common problem in AVI. Stimulated by the idea of image rain removal in visual enhancement field, this article considers the surface waterdrops, pseudodefects in essence, as a conceptual “rain-like layer.” A targeted method, namely progressive recurrent generative adversarial network (PReGAN), is designed for active waterdrop tracking and fine-grained image inpainting. Meanwhile, a steel surface database (2400 raw images with the resolution of 1000×1000 ) captured from actual hot-rolling line is constructed for the first time for open evaluation of waterdrop removal. The experimental results indicate that images enhanced by the PReGAN perform the most informative and spotless, with 52.2073 peak signal-to-noise ratio (PSNR) and 0.9502 structural similarity (SSIM) index, when compared with four prestigious networks. Assisted by the PReGAN, the false alarms are proved to be reduced at least a half during the application tests using four traditional simple detection methods.
IEEE transactions on instrumentation and measurement
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported in part by the National Natural Science Foundation of China under Grant 61973323 and Grant 6201101509, in part by the Hunan Provincial Natural Science Foundation of China under Grant 2020JJ4747, and in part by the Innovation and Development Project of Ministry of Industry and Information Technology of the People’s Republic of China under Grant TC19084DY.
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