A Review: Object Detection Models
This review paper will focus on the existing techniques that are present in the community and how each technique is different from the other techniques and perform comparative analyses of these techniques to draw meaningful conclusions.
Abstract
Object detection and recognition is an integral part of Computer Vision. It has a multitude of applications ranging from character recognition to video analysis. Object detection now play a crucial role in industries like security, video, medical, sports, and many more. With the latest research, rapid development in deep learning, and computational image understanding object detection will be more prevalent. In the future, we will be able to implement object detection in much more mundane workings of our life. Our review paper will focus on the existing techniques that are present in the community and how each technique is different from the other techniques. We will start with an introduction about object detection as a tool and technique. This will be followed by a brief background discussing the most fundamental steps involved and the basic architecture of object detection. Then, we focus on several existing techniques and perform comparative analyses of these techniques to draw meaningful conclusions. These techniques include two-stage models such as R-CNN, Fast R-CNN, and Faster R-CNN and single-stage models YOLOv1, YOLOv2, YOLOv3, YOLOv4, and SSD. Finally, we will conclude by presenting some directions that provide a scope for further analyses along that direction and hope to suggest different techniques that can be implemented in different sectors for optimal results.