mlsanityData to 14 Oct 2026

Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications

arXiv 2306.16798 ↗First posted 29 Jun 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

The objective of augmented reality (AR) is to add digital content to natural images and videos to create an interactive experience between the user and the environment. Scene analysis and object recognition play a crucial role in AR, as they must be performed quickly and accurately. In this study, a new approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time. The approach is evaluated using two datasets: a real image dataset (DOTA dataset) commonly used for computer vision tasks, and a synthetic dataset that simulates different environmental, lighting, and acquisition conditions. The focus of the evaluation is on small objects, which are difficult to detect and recognise. The results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 29 Jun 2023.

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17 of 33 references.

References · 17

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5 more
  1. 2018Object Detection with Deep Learning: A Reviewcited by 528
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  5. 2018Machine learning architectures to predict motion sickness using a Virtual Reality rollercoaster simulation toolcited by 2

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