Application of SSD network algorithm in panoramic video image vehicle detection system

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.1515/comp-2022-0270

Tao Jiang

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引用次数: 0

Abstract

Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.

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SSD网络算法在全景视频图像车辆检测系统中的应用

由于高性能摄像机的普及和计算机视频模式识别技术的发展，智能视频监控技术被广泛应用于社会生活的各个方面。主要包括:工业控制系统采用视频监控技术进行远程监控和综合监控;此外，智能视频监控技术也被广泛应用于农业领域，例如，农场管理员可以通过智能手机实时查看动物的活动情况，农业专家可以根据农作物的生长情况预测未来的天气变化。在智能监控系统的实施中，车辆图像的自动检测是一个重要的课题。中国智能交通系统的建设起步较晚，尤其是在视频交通检测方面。虽然有很多视频流量检测算法的相关研究，但这些算法通常只分析和处理来自单个传感器的信息。本文介绍了单镜头检测器(SSD)网络算法在全景视频图像车辆检测系统中的应用。本文的目的是研究SSD网络算法在全景视频图像车辆检测系统中的有效性。实验结果表明，单个卷积神经网络(CNN)算法的检测准确率仅为0.7554，召回率为0.9052，综合检测准确率为0.8235。SSD网络算法的检测准确率为0.8720，召回率为0.9397，综合检测准确率为0.9046，高于单一CNN算法。因此，将所提出的SSD网络算法与单一卷积网络算法进行了比较。它更适合于车辆检测，在全景视频图像车辆检测中起着重要的作用。

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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.