A Comparative Study of Object Detection using YOLO and SSD Algorithms

Abstract

One of the most significant and difficult areas of computer vision is object detection, which is used in many aspects of daily life, including security monitoring, autonomous vehicle operation, and so on, to find instances of semantic objects belonging to a particular class. In this survey, we first look at the current approaches of typical detection models and discuss the benchmark datasets in order to comprehensively and deeply comprehend the major development status of the object detection pipeline. This project explains how convolutional neural network-based deep learning techniques are used for object detection. In this study, deep learning methods for cutting-edge object identification systems are evaluated. Any kind of visual medium is represented by a computer as a collection of numerical numbers. In order to inspect the contents of images, they need image processing techniques. In order to determine which is the quickest and most effective object detection algorithm, this research analyzes two popular algorithms: Single Shot Detection (SSD) and You Only Look Once (YOLO). The COCO (Common Object in Context) dataset is used in this comparative research to assess the effectiveness of the two algorithms and to analyze their strengths and weaknesses in terms of factors like accuracy, precision, and speed. Based on the findings analysis, it can be said that YOLO-v3 outperforms SSD in the same testing scenario. Keywords- Object Detection, Deep learning, CNN, COCO, YOLO, SSD