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2 February 2023 Assembly training system on HoloLens using embedded algorithm
Yujin Qin, Shuxia Wang, Qiang Zhang, Yao Cheng, Jiaxu Huang, Weiping He
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Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124620H (2023) https://doi.org/10.1117/12.2660940
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China
Abstract
In this article, we demonstrate an implementation on Microsoft HoloLens, deep learning supported in the context of object detection. The main aim of the training system is to create the more accurate object detection model for augmented reality using deep learning models for image recognition directly on the HoloLens 2. In terms of the object detection approach, a deep learning model called YOLOv5 has been used for the implementation of this system. This article uses the Windows ML API to implement machine learning in augmented reality applications. A simple and easy method of drawing lines between specified 2D coordinates on a canvas is proposed. The module division and development steps of the development of augmented reality training system are given. Our system provides the annotation of augmented object detected and its bounding box via HoloLens. It allows to detect the new object in a few milliseconds. Preliminary results show a great rate of object detection and reasonable detection time.
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Yujin Qin, Shuxia Wang, Qiang Zhang, Yao Cheng, Jiaxu Huang, and Weiping He "Assembly training system on HoloLens using embedded algorithm", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124620H (2 February 2023); https://doi.org/10.1117/12.2660940
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KEYWORDS
Augmented reality
Machine learning
Data modeling
Instrument modeling
Target detection
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