Computer Vision-Based Non-Magnetic Object Detection on Moving Conveyors in Steel Industry through Differential Techniques and Performance Evaluation

Abstract

Intelligent process control technology in various manufacturing industries is important. Vision-based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent processes and raw material handling. This chapter presents an approach for a vision-based system that performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel-making industry. At single camera level, a vision-based differential algorithm is applied to recognize an object. Image pixels-based differential techniques, optical flow, and motion-based segmentations are used for traffic parameters extraction; the proposed approach extends those futures into industrial applications. The authors implement a smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection has a single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials, which are moving with raw materials, and taking immediate action at the same stage as the material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.