A Preliminary Study on Behavioral Analysis of Automobile Assembly Work Using Self-Supervised Learning

Abstract

In recent years, there has been a growing demand for analysis of worker behavior in the manufacturing industry to address labor shortages and improve work efficiency, and similar analysis is desired for automobile assembly. However, in many factories, measurement of work time and confirmation of the accuracy of procedures are still performed manually. Because of this growing importance, temporal action segmentation methods using deep neural networks have been applied to automotive assembly videos. However, supervised methods for temporal action segmentation require labels for each frame of the video, making the annotation cost extremely high compared to conventional classification tasks. Therefore, we propose a temporal action segmentation method that employs a self-supervised learning approach to analyze the behavior of automobile assembly operations from a small amount of supervised data. Experimental results show that the proposed method can perform temporal action segmentation from a small amount of supervised data.