Real-time detection method for welding parts completeness based on improved YOLOX in a digital twin environment

Abstract

Abstract Detection of the completeness of welding parts for automotive body-in-white welding relies mainly on both artificial and sensor detection. Due to a lack of intelligent methods, it is difficult to achieve accurate detection. This paper presents a new intelligent detection method based on improved YOLOX in a digital twin (DT) environment. Firstly, to address the problem of insufficient real samples, virtual datasets are made to increase data volume by using DT technology and realize the fusion of virtuality and reality. Secondly, an improved MobileNetv1 network is designed as the feature extraction network for YOLOX. Additionally, the original convolution is replaced by depthwise separable convolution blocks for reducing computational burden and improving detection speed. Experimental results show that the number of parameters is 59.1% less than that of the original model and the detection speed is increased from 36 to 50 frames s–1. Meanwhile, mean average precision increases by 1.42% and 2.76%, respectively, under two different overlaps.