Waterdrop Removal From Hot-Rolled Steel Strip Surfaces Based on Progressive Recurrent Generative Adversarial Networks

Abstract

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 $1000\\times1000$ ) 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.