Automatic monitoring of steel strip positioning error based on semantic segmentation

Abstract

The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment.