Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

Abstract

There are hundreds of welded studs in a car. The posture of a welded stud determines the quality of the body assembly, thus affecting the safety of cars. It is crucial to detect the posture of the welded studs. Considering the lack of accurate method in detecting the position of welded studs, this paper aims to detect the weld stud’s pose based on photometric stereo and neural network. Firstly, a machine vision-based stud dataset collection system is built to achieve the stud dataset labelling automatically. Secondly, photometric stereo algorithm is applied to estimate the stud normal map which as input is fed to neural network. Finally, we improve a lightweight YOLOv4 neural network which is applied to achieve the detection of stud position, thus overcoming the shortcomings of traditional testing methods. The research and experimental results show that the stud pose detection system designed achieves rapid detection and high accuracy positioning of the stud. This research provides the foundation combining the photometric stereo and deep learning for object detection in industrial production.