Detection of Scarfing Faults on the Edges of Slabs

Abstract

At present, quality control is becoming a major issue in steel production. Thus we developed an algorithm that uses machine vision to detect scarfing faults on slabs, which impairs the steel quality of subsequent products such as steel plates. Scarfing faults typically occur in three locations: the top, middle, and edge of the slab. Our proposed algorithm is focused on detecting scarfing faults on the edge of slab, which is tiny and sometimes indistinct. A machine vision system with a line scan camera was designed, which facilitates the detection of brightness differences and texture differences between well-scarfed and poorly-scarfed slab surface. Scarfing faults are tiny on the edges, so we propose a new segmentation method that takes advantage of capabilities of the line scan camera. A segmented image is filtered using Gabor filters, which were designed to focus on the boundary with scarfing faults to identify specific regions with defect, referred to as defect candidates. Each defect candidate is classified using a Support Vector Machine (SVM) classifier based on its extracted features. Our proposed algorithm was effective according to the experimental trials using 2 061 frame images acquired from real samples, where the true detection rate was 97.26% and the false detection rate was 1.66%. Our proposed system and algorithm based on machine vision technology facilitates scarfing faults detection, which can be detected before rolling process, resulting in improved steel quality.