Rain-like Layer Removal from Hot-Rolled Steel Strip Based on Attentive Dual Residual Generative Adversarial Network
Rain-like layer removal from hot-rolled steel strip surface is proved to be a workable measure for suppressing the false alarms frequently triggered in automated visual inspection (AVI) instrument. This paper extends the scope of "rain-like layer" from dispersed waterdrops to splashing wat...
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Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1 |
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Zusammenfassung: | Rain-like layer removal from hot-rolled steel strip surface is proved to be a workable measure for suppressing the false alarms frequently triggered in automated visual inspection (AVI) instrument. This paper extends the scope of "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 "rain-like layer" gathered from actual hot-rolling line, Steel_Rain, is opened for the first time. The comparison experimental results between our proposed network and eleven prestigious networks show 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 yeilds 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 1 . |
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ISSN: | 0018-9456 1557-9662 |
DOI: | 10.1109/TIM.2023.3265761 |