{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:34:43Z","timestamp":1760402083572,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,2]],"date-time":"2020-05-02T00:00:00Z","timestamp":1588377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Province Science and Technology Program Research Project","award":["No.2019YJ0174"],"award-info":[{"award-number":["No.2019YJ0174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and smart cities. In order to improve the classification accuracy of distant cars, we propose a reformed YOLOv3 (You Only Look Once, version 3) algorithm to realize the detection of various types of automobiles, such as SUVs, sedans, taxis, commercial vehicles, small commercial vehicles, vans, buses, trucks and pickup trucks. Based on the dataset UA-DETRAC-LITE, manually labeled data is added to improve the data balance. First, data optimization for the vehicle target is performed to improve the generalization ability and position regression loss function of the model. The experimental results show that, within the range of 67 m, and through scale optimization (i.e., by introducing multi-scale training and anchor clustering), the classification accuracies of trucks and pickup trucks are raised by 26.98% and 16.54%, respectively, and the overall accuracy is increased by 8%. Secondly, label smoothing and mixup optimization is also performed to improve the generalization ability of the model. Compared with the original YOLO algorithm, the accuracy of the proposed algorithm is improved by 16.01%. By combining the optimization of the position regression loss function of GIOU (Generalized Intersection Over Union), the overall system accuracy can reach 92.7%, which improves the performance by 21.28% compared with the original YOLOv3 algorithm.<\/jats:p>","DOI":"10.3390\/a13050114","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T03:29:39Z","timestamp":1588562979000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automobile Fine-Grained Detection Algorithm Based on Multi-Improved YOLOv3 in Smart Streetlights"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9450","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Deming","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhiming","family":"He","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yuanhua","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Kui","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1109\/TITS.2018.2815678","article-title":"Big Data Analytics in Intelligent Transportation Systems: A Survey","volume":"20","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/LAWP.2016.2587771","article-title":"A Compact Circularly Polarized Antenna for 5.8-GHz Intelligent Transportation System","volume":"16","author":"Maddio","year":"2017","journal-title":"Antennas Wirel. Propag. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MITS.2018.2806620","article-title":"Smart ITS Sensor for the Transportation Planning Based on IoT Approaches Using Serverless and Microservices Architecture","volume":"10","author":"Banse","year":"2018","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TITS.2018.2866970","article-title":"Scale-Free Properties of Human Mobility and Applications to Intelligent Transportation Systems","volume":"19","author":"Ferreira","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_7","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_8","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Cao, C.-Y., Zheng, J.-C., Huang, Y.-Q., Liu, J., and Yang, C.-F. (2019). Investigation of a Promoted You Only Look Once Algorithm and Its Application in Traffic Flow Monitoring. Appl. Sci., 9.","DOI":"10.3390\/app9173619"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lyu, S., Chang, M.-C., Du, D., Li, W., Wei, Y., Del Coco, M., Carcagn\u00ec, P., Schumann, A., Munjal, B., and Choi, D.-H. (2018, January 27\u201330). UA-DETRAC 2018: Report of AVSS2018 & IWT4S challenge on advanced traffic monitoring. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639089"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102907","DOI":"10.1016\/j.cviu.2020.102907","article-title":"UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking","volume":"193","author":"Wen","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_15","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 Del Coco, M. (September, January 29). UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring. Proceedings of the Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078560"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_17","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_18","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_20","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2018). mixup: Beyond Empirical Risk Minimization. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ju, M., Luo, H., Wang, Z., Hui, B., and Chang, Z. (2019). The Application of Improved YOLO V3 in Multi-Scale Target Detection. Appl. Sci., 9.","DOI":"10.3390\/app9183775"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/5\/114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:11Z","timestamp":1760361911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/5\/114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,2]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["a13050114"],"URL":"https:\/\/doi.org\/10.3390\/a13050114","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2020,5,2]]}}}