Real Time Object Detection Using Deep Learning

计算机科学 人工智能 目标检测 卷积神经网络 深度学习 帧速率 特征提取 判别式 背景(考古学) 计算机视觉 领域(数学) 视觉对象识别的认知神经科学 现场可编程门阵列 模式识别(心理学) 帧(网络) 对象(语法) 嵌入式系统 生物 电信 古生物学 纯数学 数学
作者
M. Sornalakshmi,M Sakthimohan,Elizabeth Rani. G,Vivekanandhan Aravindhan,Surya K B.,M. Devadharshni
标识
DOI:10.1109/vitecon58111.2023.10157311
摘要

Real-time object detection using deep learning has emerged as a burgeoning field of study due to its potential for a wide range of applications, including autonomous driving, robotics, and surveillance systems. The primary goal of this method is to identify interesting objects in real-world situations quickly and accurately. By utilizing convolutional brain organizations (CNNs) for highlight extraction and article identification, advanced learning-based strategies have demonstrated exceptional outcomes in this area. CNNs are trained on large-scale image datasets to learn discriminative features that capture object appearance and context effectively. The features extracted by the CNN are then used to detect objects using a detection algorithm. The Region-based Convolutional Neural Network (R-CNN) framework is one popular approach, which first proposes a set of candidate regions and then applies a CNN to each region to extract features for classification and localization. Faster CNN architectures such as Single Shot Detector (SSD) and You Only Look Once (YOLO), as well as hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs), have been proposed as ways to improve real-time performance. These methods allow for high frame rates and real-time object detection, making them suitable for a wide range of real-world applications.
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