Object Detection Using SSD

Abstract

Object identification in computer vision is a crucial task with applications in many areas, including robotics, surveillance, and self-driving cars. Deep learning architecture called Single Shot Multibox Detector (SSD) has shown to be effective for computer vision tasks. An SSD is a type of neural network-based object detection technique. The bounding boxes and class labels of the items in a picture are predicted using a single convolutional neural network (CNN). This paper presents a thorough analysis of SSD-based object detection, including its architecture, training procedure, and assessment metrics. We also talk about how the SSD framework may be expanded and modified to perform better on diverse datasets. Our results show that SSD is an effective and efficient object detection algorithm with excellent performance on several benchmark datasets.