Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

Xuan Zhang, Guohui Wang
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引用次数: 17

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 labeling 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.
基于光度立体和轻量级YOLOv4的螺柱姿态检测
一辆汽车上有数百个焊接的双头螺栓。焊接双头螺栓的姿态决定了车身总成的质量,从而影响汽车的安全性。检测焊接螺柱的姿态至关重要。针对目前焊接螺柱位置检测方法不准确的问题,本文提出了基于光度立体和神经网络的焊接螺柱姿态检测方法。首先,建立了一个基于机器视觉的鞋钉数据集采集系统,实现了鞋钉数据的自动标注。其次,将光度立体算法应用于鞋钉法线图的估计,并将其作为输入提供给神经网络。最后,我们改进了一种轻量级的YOLOv4神经网络,并将其应用于螺柱位置的检测,从而克服了传统检测方法的不足。研究和实验结果表明,所设计的鞋钉姿态检测系统实现了鞋钉的快速检测和高精度定位。该研究为工业生产中的目标检测提供了结合光度立体和深度学习的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
8.70
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