A comprehensive analysis of recent advancements in object detection

Abstract

Object detection is a fundamental problem in computer vision. Researchers are shifting towards searching the models using a neural architecture search instead of hand-crafting a model to save their effort and time. These models prove to give better accuracy and precision by using fewer FLOPs. In this paper, we analyze the recent object detection models. This paper explains the different advantages and disadvantages of one-stage and two-stage detectors. In this paper, we cover the problem solved and the advantages of each model. The paper also covers the factors affecting the performance of Object Detection models. It is followed by explaining the architecture and performance of every model. Finally, we discuss the best model by comparing them in terms of accuracy, precision, and other aspects.