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dc.creatorLi, Y
dc.creatorSong, B
dc.creatorKang, X
dc.creatorDu, X
dc.creatorGuizani, M
dc.date.accessioned2020-12-11T21:06:35Z
dc.date.available2020-12-11T21:06:35Z
dc.date.issued2018-12-01
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4349
dc.identifier.other30572635 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4367
dc.description.abstract© 2018 by the authors. Licensee MDPI, Basel, Switzerland. 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.
dc.format.extent4500-4500
dc.language.isoen
dc.relation.haspartSensors (Switzerland)
dc.relation.isreferencedbyMDPI AG
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectvehicle classification
dc.subjecttarget detection
dc.subjectcompressed sensing
dc.subjectconvolutional neural network
dc.subjectsaliency map
dc.titleVehicle-type detection based on compressed sensing and deep learning in vehicular networks
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3390/s18124500
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidDu, Xiaojiang|0000-0003-4235-9671
dc.date.updated2020-12-11T21:06:31Z
refterms.dateFOA2020-12-11T21:06:35Z


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