Testing of a Deep Learning Model Providing Monocular Depth Estimation on Mobile Devices via Web Service

Abstract

Augmented reality applications running on smartphones or tablets are becoming increasingly common. It is crucial to extract the physical structure of the scene perceived by the device camera in these applications. In such applications, employed in education, navigation and obstacle notification, the distance information between the device camera and the object must be derived and processed with sufficient accuracy and speed. In this study, the deep learning model, named "From Big To Small (BTS)", with superior performance metrics in depth extraction according to the literature reviews, was transformed into a web service and tested on an Android phone. Thus, a deep learning model with a high computational cost will be available on an Android device with average processing power. Test results were examined, and improvements were discussed.