{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:34:52Z","timestamp":1780500892656,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T00:00:00Z","timestamp":1545177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772387, 61802296"],"award-info":[{"award-number":["61772387, 61802296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds of Ministry of Education and China Mobile","award":["MCM20170202"],"award-info":[{"award-number":["MCM20170202"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M620438"],"award-info":[{"award-number":["2017M620438"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18124500","type":"journal-article","created":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T12:12:44Z","timestamp":1545221564000},"page":"4500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Vehicle-Type Detection Based on Compressed Sensing and Deep Learning in Vehicular Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Yinghua","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8096-3370","authenticated-orcid":false,"given":"Bin","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojiang","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, B., Wolfson, O., and Lin, J. (2010, January 8\u201310). Multimedia data in hybrid vehicular networks. Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia, New York, NY, USA.","DOI":"10.1145\/1971519.1971540"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13439","DOI":"10.1109\/ACCESS.2018.2810264","article-title":"CS-CNN: Enabling Robust and Efficient Convolutional Neural Networks Inference for Internet-of-Things Applications","volume":"99","author":"Shen","year":"2018","journal-title":"IEEE Access."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_4","unstructured":"Candes, E.J., and Donoho, D.L. (2006, January 22\u201330). Compressive sampling. Proceedings of the International Congress of Mathematicians, Madrid, Spain."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Candes","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","article-title":"An introduction to compressive sampling","volume":"52","author":"Candes","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.procs.2018.04.281","article-title":"Vehicle type detection based on deep learning in traffic scene","volume":"131","author":"Li","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qin, H. (2016, January 27\u201330). Joint training of cascaded CNN for face detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.376"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Triantafyllidou, D., and Tefas, A. (2016, January 4\u20138). Face detection based on deep convolutional neural networks exploiting incremental facial part learning. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900186"},{"key":"ref_13","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_15","unstructured":"Sermanet, P., and Eigen, D. (2014). OverFeat: Integrated recognition, localization and detection using convolutional networks. Advances in Neural Information Processing Systems, ICLR Press."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1109\/JIOT.2017.2690522","article-title":"Achieving Efficient and Secure Data Acquisition for Cloud-supported Internet of Things in Smart Grid","volume":"4","author":"Guan","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MCOM.2007.4378332","article-title":"Internet Protocol Television (IPTV): The Killer Application for the Next Generation Internet","volume":"45","author":"Xiao","year":"2007","journal-title":"IEEE Commun. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, H., Du, X., Li, P., and Yu, X. (2013, January 14\u201319). Prometheus: Privacy-Aware Data Retrieval on Hybrid Cloud. Proceedings of the IEEE INFOCOM 2013, Turin, Italy.","DOI":"10.1109\/INFCOM.2013.6567072"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1109\/TWC.2009.060598","article-title":"A Routing-Driven Elliptic Curve Cryptography based Key Management Scheme for Heterogeneous Sensor Networks","volume":"8","author":"Du","year":"2009","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_20","unstructured":"Szegedy, C., Toshev, A., and Erhan, D. (2013). Deep Neural Networks for Object Detection. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Erhan, D. (2014, January 24\u201327). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Uijlings, J.R., Sande, K.E., and Gevers, T. (2013). Selective search for object recognition. Proc. Int. J. Comput. Vis., 115\u2013117.","DOI":"10.1007\/s11263-013-0620-5"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rirshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., and Girshick, R. (2016, January 27\u201330). You only look once: Unified real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., and Girshick, R. (2016, January 27\u201330). SSD: single shot multibox detector. Proceedings of the IEEE Conference on Com-puter Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_26","first-page":"395","article-title":"A rapid learning algorithm for vehicle classification","volume":"295","author":"Wen","year":"2015","journal-title":"Inf. Sci. Int. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, S., Wu, Y., and Li, W. (2017). A model for fine-grained vehicle classification based on deep learning. Neurocomputing, 257.","DOI":"10.1016\/j.neucom.2016.09.116"},{"key":"ref_28","unstructured":"MIT (2018, October 24). MIT Pedestrian Data [EB\/OL]. [2014-01-01]. Available online: http:\/\/cbcl.mit.edu\/software-datasets\/PedestrianData.html."},{"key":"ref_29","unstructured":"Caltech (2018, October 25). Caltech Pedestrian Detection Benchmark [EB\/OL]. [2014-01-01]. Available online: http:\/\/www.vision.caltech.edu\/Image_Datasets\/CaltechPedestrians\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1049\/iet-ipr.2013.0380","article-title":"Estimation of measurements for block-based compressed video sensing: Study of correlation noise in measurement domain","volume":"8","author":"Song","year":"2014","journal-title":"Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/TMM.2016.2564100","article-title":"Significance Evaluation of Video Data Over Media Cloud Based on Compressed Sensing","volume":"18","author":"Guo","year":"2016","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., and Ren, S. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","unstructured":"Lienhart, R., and Maydt, J. (2002, January 22\u201325). An extended set of Haar-like features for rapid object detection. Proceedings of the IEEE International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lienhart, R., Kuranov, A., and Pisarevsky, V. (2003, January 10\u201312). Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Proceedings of the 25th German Pattern Recognition Symposium, Magdeburg, Germany.","DOI":"10.1007\/978-3-540-45243-0_39"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4500\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T16:51:21Z","timestamp":1775321481000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4500"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,19]]},"references-count":34,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124500"],"URL":"https:\/\/doi.org\/10.3390\/s18124500","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,19]]}}}