Research on YOLO-based Component Detection Algorithms

Abstract

Components are the foundation of machine manufacturing, and domestic manufacturing enterprises require a wide variety of parts in large quantities during production. In industrial production, the inspection of parts is a critical step. This paper primarily addresses the issue of part inspection, focusing on industrial components as the research subject. Building upon the research and analysis of object detection in the field of deep learning, the YOLOv5 detection model is optimized. The experimental results indicate that the optimized model exhibits high accuracy and detection efficiency. It also demonstrates the superiority of deep learning-based object detection methods in the field of irregular precision component inspection, compared to traditional object detection algorithms.