{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T13:10:01Z","timestamp":1776690601665,"version":"3.51.2"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"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":["61601466"],"award-info":[{"award-number":["61601466"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011363","name":"State Key Laboratory of Coal Resources and Safe Mining","doi-asserted-by":"publisher","award":["SKLCRSM16KFD04"],"award-info":[{"award-number":["SKLCRSM16KFD04"]}],"id":[{"id":"10.13039\/501100011363","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2016QJ04"],"award-info":[{"award-number":["2016QJ04"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 \u00d7 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 \u00d7 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.<\/jats:p>","DOI":"10.3390\/s19030594","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T03:08:05Z","timestamp":1548990485000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-3478","authenticated-orcid":false,"given":"Fukai","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3081-6751","authenticated-orcid":false,"given":"Ce","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIP.2017.2762591","article-title":"Multi-Task Vehicle Detection With Region-of-Interest Voting","volume":"27","author":"Chu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., and Heng, P.A. (arXiv, 2018). SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection, arXiv.","DOI":"10.1109\/TITS.2018.2838132"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3491","DOI":"10.1109\/ACCESS.2017.2782159","article-title":"Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning","volume":"6","author":"Loukas","year":"2018","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TITS.2010.2040177","article-title":"A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking","volume":"11","author":"Sivaraman","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TITS.2012.2187894","article-title":"On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation","volume":"13","author":"Niknejad","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (arXiv, 2015). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, arXiv.","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jeong, J., Park, H., and Kwak, N. (arXiv, 2017). Enhancement of SSD by concatenating feature maps for object detection, arXiv.","DOI":"10.5244\/C.31.76"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ghodrati, A., Diba, A., Pedersoli, M., Tuytelaars, T., and Gool, L.V. (2015, January 7\u201313). DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.296"},{"key":"ref_15","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (arXiv, 2014). Going Deeper with Convolutions, arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_17","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u201312). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the Neural Information Processing Systems (NIPS), Montr\u00e9al, QC, Canada."},{"key":"ref_18","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Erhan, D., Szegedy, C., Toshev, A., and Anguelov, D. (2014, January 23\u201328). Scalable Object Detection using Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.276"},{"key":"ref_20","unstructured":"Szegedy, C., Reed, S., Erhan, D., Anguelov, D., and Ioffe, S. (arXiv, 2015). Scalable, High-Quality Object Detection, arXiv."},{"key":"ref_21","unstructured":"Bell, S., Zitnick, L., Bala, K., and Girshick, R. (July, January 26). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_22","unstructured":"Liu, W., Rabinovich, A., and Berg, A.C. (arXiv, 2015). ParseNet: Looking Wider to See Better, arXiv."},{"key":"ref_23","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (July, January 26). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2016). YOLO9000: Better, Faster, Stronger, arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_25","unstructured":"Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (arXiv, 2017). DSSD: Deconvolutional Single Shot Detector, arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (arXiv, 2018). Single-Shot Refinement Neural Network for Object Detection, arXiv.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, R., Shan, S., and Chen, X. (2018, January 18\u201322). Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00730"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, P. (2018, January 18\u201322). Scale-Transferrable Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00062"},{"key":"ref_29","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2018). YOLOv3: An Incremental Improvement, arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TCYB.2013.2248057","article-title":"Radial basis function based neural network for motion detection in dynamic scenes","volume":"44","author":"Huang","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_31","first-page":"1","article-title":"Robust principal component analysis","volume":"58","author":"Li","year":"2011","journal-title":"J. ACM"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3047","DOI":"10.1109\/TIT.2011.2173156","article-title":"Robust PCA via outlier pursuit","volume":"58","author":"Xu","year":"2012","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ins.2014.12.033","article-title":"Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems","volume":"299","author":"Chen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., and Xue, X. (arXiv, 2017). Evolving Boxes for Fast Vehicle Detection, arXiv.","DOI":"10.1109\/ICME.2017.8019461"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22804","DOI":"10.1109\/ACCESS.2017.2756081","article-title":"Scene-Adaptive Vehicle Detection Algorithm Based on a Composite Deep Structure","volume":"5","author":"Cai","year":"2017","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1109\/TITS.2016.2598192","article-title":"Nighttime Vehicle Detection Based on Bio-Inspired Image Enhancement and Weighted Score-Level Feature Fusion","volume":"18","author":"Kuang","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TITS.2017.2784486","article-title":"Multi-Perspective Tracking for Intelligent Vehicle","volume":"19","author":"Ji","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liu, L., Shao, L., and Mellor, M. (arXiv, 2016). DAVE: A Unied Framework for Fast Vehicle Detection and Annotation, arXiv.","DOI":"10.1007\/978-3-319-46475-6_18"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, L., Luo, P., Loy, C.C., and Tang, X. (2015, January 7\u201312). A Large-Scale Car Dataset for Fine-Grained Categorization and Verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299023"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tang, T., Zhou, S., Deng, Z., Lei, L., and Zou, H. (2017). Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks. Remote Sens., 9.","DOI":"10.3390\/rs9111170"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tang, T., Zhou, S., Deng, Z., Zou, H., and Lei, L. (2017). Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining. Sensors, 17.","DOI":"10.3390\/s17020336"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1109\/JSTARS.2017.2694890","article-title":"Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks","volume":"10","author":"Deng","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21651","DOI":"10.1007\/s11042-016-4043-5","article-title":"Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks","volume":"76","author":"Qu","year":"2017","journal-title":"Multimedia Tools Appl."},{"key":"ref_45","unstructured":"Ioffe, S., and Szegedy, C. (arXiv, 2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv."},{"key":"ref_46","unstructured":"Shelhamer, E., Long, J., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., and Malik, J. (2015, January 7\u201312). Hypercolumns for Object Segmentation and Fine-grained Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"Lecun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_49","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics, Sardinia, Italy."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lyu, S., Chang, M.C., Du, D., Wen, L., Qi, H., Li, Y., Wei, Y., Ke, L., Hu, T., and Coco, M.D. (September, January 29). UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring. Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078560"},{"key":"ref_51","unstructured":"Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.C., Qi, H., Lim, J., Yang, M.H., and Lyu, S. (arXiv, 2015). UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking, arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Geiger, A., Philip, G., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (arXiv, 2014). Caffe: Convolutional Architecture for Fast Feature Embedding, arXiv.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_54","unstructured":"Fredrik, G., and Erik, L.N. (2018). Automotive 3D Object Detection Without Target Domain Annotations. [Master\u2019s Thesis, Link\u00f6ping University]."},{"key":"ref_55","unstructured":"Yang, F., Choi, W., and Lin, Y. (July, January 26). Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-View 3D Object Detection Network for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TIV.2017.2695896","article-title":"RefineNet: Refining Object Detectors for Autonomous Driving","volume":"1","author":"Rajarm","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rajarm, R., Bar, E., and Trivedi, M. (2016, January 1\u20134). RefineNet: Iterative Refinement for Accurate Object Localization. Proceedings of the Intelligent Transportation Systems Conference, Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795760"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1109\/TITS.2015.2496795","article-title":"Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework","volume":"17","author":"Hu","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_60","unstructured":"Stewart, R., Andriluka, M., and Ng, A. (July, January 26). End-to-End People Detection in Crowded Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:51Z","timestamp":1760185791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19030594"],"URL":"https:\/\/doi.org\/10.3390\/s19030594","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,30]]}}}