Analysis of Object Detection Models

Abstract

Abstract: Object detection plays a crucial role in today’s world, and it gives direction in the field of computer vision. Object detection is a fundamental task used to detect the object in the images. Object detection plays a significant role in various applications such as surveillance, medical images, and autonomous vehicles. The primary goal of object detection is to accurately detect the boundaries of objects of interest and classify those objects into predefined categories. This paper analyzes three common object detection models: Single Shot Multibox Detector (SSD), You Only Look Once (YOLOv7), and You Only Look Once (YOLOv8). In this research work, both empirical and theoretical approaches were followed. The empirical study was carried out on a set of experiments using some software tools. For the theoretical approach, a review of both secondary data and data based on results obtained by applying the tools is studied. Secondary data was acquired from the books, e-journals, reports, review papers, published theses, websites, articles, conference proceedings, survey papers, and blogs that are related to this domain. The results that were obtained from this implementation were analyzed for the findings of the research. This paper compares three object detection model’s results, which are tested on the same dataset, the same environment, and the same system. This paper found that You Only Look Once (YOLOv8) obtained the best results, followed by You Only Look Once (YOLOv7) and Single Shot Multibox Detector (SSD) at last.