A Lightweight Mobile Outdoor Augmented Reality Method Using Deep Learning and Knowledge Modeling for Scene Perception to Improve Learning Experience

Abstract

ABSTRACT Mobile augmented reality (AR) technology creates realistic learning situations and a strong sense of immersion, which is conducive to enhance learning experience and stimulate learning motivation. However, existing mobile outdoor augmented reality applications generally have a complicated operation process and a mismatch between learning resources and corresponding scenes, which leads to a poor learning experience. Therefore, we propose a lightweight mobile outdoor AR method that combines deep learning and knowledge modeling to perceive learning scenes with a goal to improve learning experience. This method improves the accuracy of scene perception and resources retrieval and provides a convenient mobile AR technology solution for outdoor learning. To evaluate the proposed method, we provide objective criteria to assess the effectiveness of the lightweight object detection model and the learning resources retrieval approach. Simultaneously, we investigate the evaluation of participants majoring in teacher education on the usability of the proposed method by the modified system usability scale questionnaire and net promoter score. Experimental results demonstrate that our method achieves high detection accuracy, good usability, and is of great significance to improve outdoor learning experience.