A Study on Automation of Work Measurement in Assembly Work Using Region of Interest and Deep Learning

Abstract

This paper focuses on the automation of work measurement using a camera to capture workers' hands in an assembly factory and analyzing the image to estimate the work content. In recent years, the technology to collect, analyze, and visualize manufacturing data from various devices installed in processing machines has been improving. However, it has not yet reached the point where workers' manual operations can be analyzed and visualized in real time. In this study, it is proposed that high estimate accuracy can be achieved using two analysis procedures to analyze a worker's image with deep learning: setting the region of interest and estimating the work content from the features in the region of interest. As a result of an evaluation experiment conducted at an actual factory using the proposed method, 99.5% of the work was correctly estimated. Based on these results, the method proposed in this study is now being examined in anticipation of introducing it for practical application.