Slag Removal Path Estimation by Slag Distribution Image and Multi-Task Deep Learning Network

This paper proposes an automatic slag removal path estimation in a ladle image using a deep neural network. Slag removal is a hazardous task in steel production, and it is conducted manually by experienced human operators. To automate the slag removal task using a robotic machine, a slag removal path in a ladle image must be determined to control the motion of the machine. We propose a novel image structure called Slag Distribution Image(SDI) and a Slag Removal Path Estimation Network(SRPENet) to estimate the slag removal path. SDI contains the slag distribution and the ladle boundary information. SRPENet is designed as a multi-task learning architecture to perform two tasks: the first task is to estimate the control point of the slag removal path and the second task is to estimate the goodness-score of the control points. The structure of SDI is designed to yield efficient performance in SRPENet. The ground truth and learning data of slag removal path are collected along the working trajectories of human experts. The final removal path is determined by post-processing of the outputs of SRPENet. In experiments using real ladle images, the amount of removed slag and the mechanical energy efficiency are evaluated and compared with the ground truth to present qualitative and quantitative analysis. In addition, two ablation studies are presented. The first study is using SDI or RGB image as the input of SRPENet. The second study is the presence or absence of the smooth term in the SRPENet’s loss function. The proposed method shows 119.83% and 96.2% performance compared to ground truth in the amount of removed slag and mechanical energy efficiency, respectively. Our method runs in real-time with about 53.168 msec/frame.