U-Net for wheel rim contour detection in robotic deburring

Abstract

Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wheel rim contours under various lighting and background conditions, ensuring sharp and accurate segmentation, crucial for automotive manufacturing processes. Our experiments indicate that our method surpasses conventional techniques in terms of precision and efficiency, representing a significant contribution to the incorporation of deep learning in industrial automation. Specifically, our method reduces segmentation errors and improves the efficiency of the deburring process, which is essential for maintaining quality and productivity in modern production lines.