Image/Object Detection Using Shot Multi-Box Detector (SSMBD) Algorithm

Abstract

Abstract: Object identification, is one of most important roles in computer vision, has been a hotspot for research and application over the past 20 years. Its purpose is to recognise and locate a large number of items in a given environment that fall into specific categories rapidly and consistently. It has gained a lot of study attention because of its tight association with image and video analysis. Its purpose is to discover and locate a large number of things in a given image that belong to specified categories rapidly and consistently. More sophisticated tools that can learn semantic, high-level, richer aspects are being offered to address the existing issues as deep learning advances. There are several types of algorithms. Based on the model training approach, the algorithms can be divided into two categories: single-stage detection algorithms and two-stage detection algorithms. Our investigation begins with an overview of deep learning and its most prominent tool, the Convolutional Neural Network (CNN). We'll also look at a common traditional object detection framework, along with some variations. Other tasks such as face detection and object tracking, as well as other important characteristics, would be implemented. Thus, future work in both object detection and relevant neural network-based learning systems should adhere to these guidelines and be useful. Important terms: SSD, Convolution Neural Network, YOLO