Augmented Reality with Mask R-CNN (ARR-CNN) inspection for Intelligent Manufacturing

Abstract

A machine is an essential factor for industrial production. Industry 4.0 is the revolution that causes improvement of machines to have higher efficiency. Accordingly, inspection and maintenance are becoming more important. However, most of factories are not changed the operating process, there is no data logging for evaluation and analysis for preventive maintenance. This research aims to develop a model for machine inspection using augmented reality with object detection and marker techniques on real world machines and mask R-CNN algorithm allowing inspector to perform inspections. This study, we demonstrate the process of development of the proposed model by showing steps of data acquisition from a machine in a factory. The dataset is images of machines in different perspectives, and they were used for training and testing the model. The testing is done on a mobile device of an inspector. With computer vision technique and the proposed model, the instant precision tracking and detection are provided. Then the trained model is transferred to the mobile devices for testing without any modification by an expert. Some images of machines are randomly selected to verify the accuracy of the model. The result shows that the efficiency of the model is acceptable in real usage.