Development and Optimization of Automotive Testing Using Machine Vision

In this paper, a computer vision-based approach for optimizing component test benches in endurance testing of automotive components. As a use case, the paper explores testing of automotive throttle position sensor using fiducial markers and image-processing techniques. The proposed methodology is highly scalable and cost effective while maintaining the required testing accuracy. This research contributes to advancing computer vision techniques in optimizing testing methodologies in bulk manufacturing industries like the automotive industry.