Pedestrian detection system based on deep learning

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-09-01 DOI:10.11591/ijaas.v11.i3.pp194-198

Mohammed Razzok, A. Badri, Ilham El Mourabit, Y. Ruichek, A. Sahel

引用次数: 3

Abstract

Pedestrian detection is a rapidly growing field of computer vision with applications in smart cars, surveillance, automotive safety, and advanced robotics. Most of the success of the last few years has been driven by the rapid growth of deep learning, more efficient tools capable of learning semantic, high-level, deeper features of images are proposed. In this article, we investigated the task of pedestrian detection on roads using models based on convolutional neural networks. We compared the performance of standard state-of-the-art object detectors like Faster region-based convolutional network (R-CNN), single shot detector (SSD), and you only look once, version 3 (YOLOv3). Results show that YOLOv3 is the best object detection model than others for pedestrians in terms of detection and time prediction.

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基于深度学习的行人检测系统

行人检测是一个快速发展的计算机视觉领域，在智能汽车、监控、汽车安全和先进机器人等领域都有应用。过去几年的大部分成功都是由深度学习的快速发展所推动的，人们提出了能够学习图像语义、高级、更深层次特征的更有效的工具。在本文中，我们研究了基于卷积神经网络模型的道路行人检测任务。我们比较了标准的最先进的目标检测器的性能，如更快的基于区域的卷积网络(R-CNN)，单镜头检测器(SSD)，你只看一次，版本3 (YOLOv3)。结果表明，YOLOv3在行人目标检测和时间预测方面优于其他模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.