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

TL;DR

A new approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time and tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.

Abstract

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.

Figures (4)

• Figure 1: An abstract of the FRRCNN architecture.
• Figure 2: An abstract of the YOLOv3 architecture.
• Figure 3: An abstract of the YOLOv3 architecture.
• Figure 4: An abstract of the YOLOv5 with Oriented Bounding Boxes (OBB) architecture.