  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.
