期刊论文详细信息
Applied Sciences | |
Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training | |
Sidike Paheding1  John Estrada2  Quamar Niyaz2  Xiaoli Yang2  | |
[1]Department of Applied Computing, Michigan Technological University, Houghton, MI 49931, USA | |
[2]Department of Electrical and Computer Engineering, Purdue University Northwest, Hammond, IN 46323, USA | |
关键词: artificial intelligence; augmented reality; machine learning; object detection; computer in education; lab equipment tutorial; | |
DOI : 10.3390/app12105159 | |
来源: DOAJ |
【 摘 要 】
Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow’s object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is【 授权许可】
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