Vehicle-Type Detection Based on Compressed Sensing and Deep Learning in Vehicular Networks
Vehicle-Type Detection Based on Compressed Sensing and Deep Learning in Vehicular Networks
复制标题
基于车载网络压缩感知和深度学习的车型检测
DOI：
10.3390/s18124500
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发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
Guizani Mohsen
中科院分区：
文献类型：
--
作者：
Li Yinghua;Song Bin;Kang Xu;Du Xiaojiang;Guizani Mohsen
在过去的十年中，车载网络引起了各个领域的极大兴趣。车辆数量的增加给交通监管带来了挑战。车辆类型检测是一个重要的研究课题，在许多领域都有不同的应用。其主要目的是从交通监控拍摄的视频或图片中提取车辆的不同特征，从而识别车辆的类型，进而为交通监控和控制提供参考信息。在本文中，我们提出了一种使用显着图和卷积神经网络（CNN）技术的步进车辆检测和分类方法。具体来说，应用压缩感知（CS）理论生成显着图来标记图像中的车辆，然后使用CNN方案对它们进行分类。我们应用显着图的概念来搜索图像中的目标车辆：此步骤基于使用显着图来最小化冗余区域。 CS用于测量感兴趣的图像并获得其在测量域中的显着性。由于测量域中的数据比像素域中的数据小得多，因此可以以较低的计算成本和更快的速度生成显着图。然后，根据显着图，我们识别出目标车辆，并使用 CNN 将其分类为不同类型。实验结果表明，我们的方法能够加速基于 CNN 的图像分类的窗口校准阶段。此外，与其他方法相比，我们提出的方法在车辆类型检测方面具有更好的整体性能。它在车载网络的实际应用中具有非常广阔的前景。
Throughout the past decade, vehicular networks have attracted a great deal of interest in various fields. The increasing number of vehicles has led to challenges in traffic regulation. Vehicle-type detection is an important research topic that has found various applications in numerous fields. Its main purpose is to extract the different features of vehicles from videos or pictures captured by traffic surveillance so as to identify the types of vehicles, and then provide reference information for traffic monitoring and control. In this paper, we propose a step-forward vehicle-detection and -classification method using a saliency map and the convolutional neural-network (CNN) technique. Specifically, compressed-sensing (CS) theory is applied to generate the saliency map to label the vehicles in an image, and the CNN scheme is then used to classify them. We applied the concept of the saliency map to search the image for target vehicles: this step is based on the use of the saliency map to minimize redundant areas. CS was used to measure the image of interest and obtain its saliency in the measurement domain. Because the data in the measurement domain are much smaller than those in the pixel domain, saliency maps can be generated at a low computation cost and faster speed. Then, based on the saliency map, we identified the target vehicles and classified them into different types using the CNN. The experimental results show that our method is able to speed up the window-calibrating stages of CNN-based image classification. Moreover, our proposed method has better overall performance in vehicle-type detection compared with other methods. It has very broad prospects for practical applications in vehicular networks.
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DOI：
10.1017/cbo9780511794308
发表时间：
2012
期刊：
--
影响因子：
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作者：
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DOI：
10.1109/infcom.2013.6567072
发表时间：
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期刊：
2013 Proceedings IEEE INFOCOM
影响因子：
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DOI：
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发表时间：
2022-11
期刊：
Highlights in Science, Engineering and Technology
影响因子：
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影响因子：
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影响因子：
10.6
作者：
Guan, Zhitao;Li, Jing;Du, Xiaojiang
通讯作者：
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