Vehicle Detection System using YOLOv4

In today’s world, a commonly used computer technology - object detection focuses on finding items or examples of a specific class such as humans, vehicles, and animals in digital photos. Most computer vision systems require the capacity to detect objects. Object detection is performed often in computer vision tasks such as face detection, face identification, and video object co-segmentation, as well as in tracking people, surveillance cameras, and driverless cars. It is the fundamental notion for tracking and identifying things, and it has an impact on object recognition efficiency and accuracy. The major contribution of this paper is i) Comparison of recent research works in the domain of vehicle detection and ii) Classification of vehicles into 2 specified classes – car and bus are performed on a dataset of 10,000 images. The YOLOv4 model has been deployed for detecting cars and buses.