关键词: 增强现实, 深度学习, 快速区域卷积神经网络（Faster R-CNN）, 诱导维修, 自动交互

Abstract: With the development of augmented reality technology in the field of machinery, more and more examples have proved the superiority of AR in improving the operational efficiency in industrial maintenance. In order to improve the efficiency of the induced maintenance operation process, an automatic interaction of process recognition based on Faster Regional Convolutional Neural Network（Faster R-CNN） is proposed for the problem that the traditional augmented reality maintenance system cannot sense and judge the maintenance status. The method is based on Faster R-CNN to establish a deep neural network model for part recognition and further fine-tuning by means of back propagation. By identifying the type and number of the parts, the feedback is given to the system to trigger the corresponding operation steps without additional interaction by the user operating. The experimental results show that the recognition rate of repair parts based on deep neural network can reach 95%, and the average recognition speed is 300 ms per frame, which meets the accuracy and interactivity requirements of AR-induced maintenance system.

Key words: augmented reality, deep learning, Faster Regional Convolutional Neural Network（Faster R-CNN）, induced maintenance, automatic interaction