基于深度学习方法的目标检测与识别系统

Yashal Railkar, Aditi Nasikkar, Sakshi Pawar, P. Patil, Rohini. G. Pise
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引用次数: 2

摘要

许多研究人员已经研究了物体检测在工业中的重要应用,如自动驾驶汽车的道路物体检测,检测特定疾病的医学研究,手势控制等。目标检测和识别在安全方面是非常重要的。由于计算机和模型可以全天候工作,它可以在安全区域观看视频监控。人类可以快速检测或识别照片和照片中的物品,这些图像和照片位于何处,以及当他们看到它们时如何与系统交互。[1]。目标识别和跟踪是CV系统和交互中的一个关键挑战,例如视觉监控和人类计算机视觉系统。人类的视觉系统是快速和精确的,使他们能够处理复杂的活动,如驾驶。计算机将能够使用即兴的、快速无误的目标识别算法来驾驶汽车,但它们将需要专门的传感器和辅助设备来传递实时场景。[1]使用精确的目标识别和图像分类方法、策略和方法,对于在大都市情况下决定自动驾驶至关重要。许多大公司目前都在努力实现这一目标。本文提出了一种可以检测各种物体的目标检测系统,实际上它几乎可以检测任何物体。对模型进行训练。报告中提出的目标检测方法是You Look Only Once (YOLO)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object Detection and Recognition System Using Deep Learning Method
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).
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