Rain-Like Layer Removal From Hot-Rolled Steel Strip Based on Attentive Dual Residual Generative Adversarial Network

Abstract

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eleven</i> prestigious networks show that our ADRGAN-restored images are the closest to the ground-truth images on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">six</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">four</i> traditional defect detection algorithms are involved. And as expected, defect detection methods assisted by ADRGAN yield the minimum false-alarms <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .