University of Oulu

Q. Luo, H. He, K. Liu, C. Yang, O. Silvén and L. Liu, "Rain-Like Layer Removal From Hot-Rolled Steel Strip Based on Attentive Dual Residual Generative Adversarial Network," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15, 2023, Art no. 5011715, doi: 10.1109/TIM.2023.3265761

Rain-like layer removal from hot-rolled steel strip based on attentive dual residual generative adversarial network

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Author: Luo, Qiwu1; He, Handong1; Liu, Kexin1;
Organizations: 1School of Automation, Central South University, Changsha, China
2Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
3College of Electronic Science, National University of Defense Technology, Changsha, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-05-26


Rain-like layer removal from hot-rolled steel strip surface has been proven to be a workable measure for suppressing the false alarms frequently triggered in automated visual inspection (AVI) instruments. This article extends the scope of the “rain-like layer” from dispersed waterdrops to splashing water streaks and tiny white droplets. And a targeted method with both channel-wise and spatial-wise attention, namely attentive dual residual generative adversarial network (ADRGAN), is proposed. Meanwhile, a newly updated steel surface image dataset with typical natures of a “rain-like layer” gathered from an actual hot-rolling line, Steel_Rain, is opened for the first time. The comparison of experimental results between our proposed network and 11 prestigious networks shows that our ADRGAN-restored images are the closest to the ground-truth images on six public datasets, especially on the newly opened industrial dataset Steel_Rain; it yields the best scores of 56.8627 peak signal to noise ratio (PSNR), 0.9980 structural similarity index (SSIM), 0.134 mean-square error (MSE) and 0.006 learned perceptual image patch similarity (LPIPS). In the final verification test, the concept of rain-like layer removal has been proved to perform best in defect inspection, where four traditional defect detection algorithms are involved. And as expected, defect detection methods assisted by ADRGAN yield the minimum false alarms.

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Series: IEEE transactions on instrumentation and measurement
ISSN: 0018-9456
ISSN-E: 1557-9662
ISSN-L: 0018-9456
Volume: 72
Article number: 5011715
DOI: 10.1109/TIM.2023.3265761
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported jointly by the National Natural Science Foundation of China (Grant Number: 61973323 and 62111530071); Hunan Provincial Natural Science Foundation of China (Grant Number: 2021JJ20078); Science and Technology Innovation Program of Hunan Province (Grant Number: 2021RC3019).
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