Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4  

作　　者：Xuan Zhang Guohui Wang 

机构地区：[1]School of Opto-Electronic Engineering,Xi’an Technological University,Xi’an 710021,China

出　　处：《Journal of Artificial Intelligence and Technology》2022年第1期32-37,共6页人工智能技术学报（英文）

基　　金：The work is partly supported by the Natural Science Basic Research Plan in Shaanxi Province of China(No.2016JM6041).

摘　　要：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.

关 键 词：Stud pose photometric stereo neural network machine vision 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]