Miniscule Object Detection in Aerial Images Using YOLOR: A Review

Object detection has made immense improvements in natural images during the last decade but not so much in aerial images. Detection of miniature objects in aerial images remains challenging as they contain only a few pixels and extremely large input sizes. Moreover, tiny objects are easily fooled by the backstory and increase the difficulty of accurate detection. Many algorithms are used for object detection purposes, and YOLOR is one of them. YOLOR "You Only Learn One Representation" is a one-stage detector. It is specially made for object detection, whereas other algorithms include object classification or analysis. In CNN, only, one task is carried out at a time, whereas YOLOR is a unified model useful for multitasking purposes. In this paper, we discussed tiny object detection in aerial images using YOLOR. Based on our research, we found that the AI-TOD dataset contains object instances in eight categories, with 86% of the objects being smaller than 16 pixels in size. The AI-TOD can be used to assess the performance of a variety of small objects. The mean size of objects is approximately 12.8 pixels, which is considerably smaller than the other datasets.