Object Detection and Recognition System Using Deep Learning Method

Abstract

Object detection has been studied by many researchers for important applications in the industry like detecting a road object for self-driving cars, medical research for detecting particular diseases, gesture control, etc. Object detection and recognition is incredibly very important wrt security purposes. As computers and models can work 24/7 it can watch for video surveillance in secure areas. Humans can quickly detect or make out what items are there in photos and photographs, where these images and pictures are located, and how they interact with systems when they see them. [1]. Object identification and tracking is a key challenge in CV systems and interactions, such as visual surveillance and human computer vision systems. Human visual systems are quick and precise, allowing them to handle complicated activities such as driving. Computers will be able to drive automobiles using improvised and speedy errorfree object identification algorithms, yet they will require specialized sensors and auxiliary gadgets to relay real-time scenarios. [1]Using exact object recognition and picture classification approaches, strategies, and methodologies, it is critical and essential for deciding autonomous driving in metropolitan situations. Many big companies are currently working on this and achieving their goals day by day. In this report a object detection system has been proposed which can detect various objects, in fact it can detect almost any object wrt. training given to the model. Proposed methodology for object detection in the report is You Look Only Once (YOLO).