A Deep Learning Module Design for Workspace Identification in Manufacturing Industry

Abstract

In this paper, in order to solve various problems occurring in the workspace, a deep learning-based workspace identification module was designed, and the performance was analyzed through an experiment on the recognition accuracy according to the configuration of the training dataset and the number of training. The data model of the designed deep learning module is ResNetl8, and after setting up three dataset strategies, a dataset using five types of workspaces of the manufacturing industry was selected. In terms of the average top 5 and all training, strategy 2 was 81.2% and 76.4%, respectively, confirming that it was the best among the 3 strategies. In the future, after upgrading the designed module, it is planned to implement a module with real-time workspace identification performance level of practical use in a mobile environment with an image input device installed.