{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:04:29Z","timestamp":1781885069546,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T00:00:00Z","timestamp":1564444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFB0802400"],"award-info":[{"award-number":["2017YFB0802400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle detection is a challenging task in computer vision. In recent years, numerous vehicle detection methods have been proposed. Since the vehicles may have varying sizes in a scene, while the vehicles and the background in a scene may be with imbalanced sizes, the performance of vehicle detection is influenced. To obtain better performance on vehicle detection, a multi-scale vehicle detection method was proposed in this paper by improving YOLOv2. The main contributions of this paper include: (1) a new anchor box generation method Rk-means++ was proposed to enhance the adaptation of varying sizes of vehicles and achieve multi-scale detection; (2) Focal Loss was introduced into YOLOv2 for vehicle detection to reduce the negative influence on training resulting from imbalance between vehicles and background. The experimental results upon the Beijing Institute of Technology (BIT)-Vehicle public dataset demonstrated that the proposed method can obtain better performance on vehicle localization and recognition than that of other existing methods.<\/jats:p>","DOI":"10.3390\/s19153336","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T11:15:56Z","timestamp":1564485356000},"page":"3336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7916-4259","authenticated-orcid":false,"given":"Zhongyuan","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8703-7310","authenticated-orcid":false,"given":"Jun","family":"Sang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Cai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/TIP.2007.891147","article-title":"Vehicle Detection Using Normalized Color and Edge Map","volume":"16","author":"Tsai","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TITS.2011.2113340","article-title":"Vehicle Detection and Tracking in Car Video Based on Motion Model","volume":"12","author":"Jazayeri","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cao, X., Wu, C., Yan, P., and Li, X. (2011, January 11\u201314). Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos. Proceedings of the 2011 IEEE International Conference Image Processing (ICIP), Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116132"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guo, E., Bai, L., Zhang, Y., and Han, J. (2017, January 13\u201315). Vehicle Detection Based on Superpixel and Improved HOG in Aerial Images. Proceedings of the International Conference on Image and Graphics, Shanghai, China.","DOI":"10.1007\/978-3-319-71607-7_32"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Laopracha, N., and Sunat, K. (2017, January 21\u201323). Comparative Study of Computational Time that HOG-Based Features Used for Vehicle Detection. Proceedings of the International Conference on Computing and Information Technology, Helsinki, Finland.","DOI":"10.1007\/978-3-319-60663-7_26"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_7","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pyo, J., Bang, J., and Jeong, Y. (2016, January 23\u201326). Front collision warning based on vehicle detection using CNN. Proceedings of the International SoC Design Conference (ISOCC), Jeju, Korea.","DOI":"10.1109\/ISOCC.2016.7799842"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5817","DOI":"10.1007\/s11042-015-2520-x","article-title":"Vehicle Detection and Recognition for Intelligent Traffic Surveillance System","volume":"76","author":"Tang","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gao, Y., Guo, S., Huang, K., Chen, J., Gong, Q., Zou, Y., Bai, T., and Overett, G. (2017, January 11\u201314). Scale optimization for full-image-CNN vehicle detection. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995812"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huttunen, H., Yancheshmeh, F.S., and Chen, K. (2016, January 19\u201322). Car type recognition with deep neural networks. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535529"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (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, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2014, January 6\u201312). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Proceedings of the 2014 IEEE International Conference of European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2016). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv.","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref_19","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u20138). R-FCN: Object Detection via Region-Based Fully Convolutional Networks. Proceedings of the 2016 IEEE International Conference of Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Azam, S., Rafique, A., and Jeon, M. (2016, January 27\u201329). Vehicle Pose Detection Using Region Based Convolutional Neural Network. Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS), Ansan, Korea.","DOI":"10.1109\/ICCAIS.2016.7822459"},{"key":"ref_21","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_22","first-page":"3652","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.-Stars."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). 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.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot Multibox Detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_26","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An Incremental Improvement. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sang, J., Wu, Z., Guo, P., Hu, H., Xiang, H., Zhang, Q., and Cai, B. (2018). An Improved YOLOv2 for Vehicle Detection. Sensors, 18.","DOI":"10.3390\/s18124272"},{"key":"ref_28","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). k-means plus plus: The Advantages of Careful Seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_29","first-page":"100","article-title":"Algorithm AS 136: A k-means Clustering Algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc."},{"key":"ref_30","unstructured":"Carlet, J., and Abayowa, B. (2018). Fast vehicle detection in aerial imagery. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1109\/TITS.2015.2402438","article-title":"Vehicle type classification using a semisupervised convolutional neural network","volume":"16","author":"Dong","year":"2015","journal-title":"IEEE Trans. Intell. 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