{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:58:30Z","timestamp":1782453510129,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China.","award":["2019YFC1521300"],"award-info":[{"award-number":["2019YFC1521300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F1\u00a0\u2212\u00a0Score. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.<\/jats:p>","DOI":"10.3390\/s21227545","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"7545","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2883-7211","authenticated-orcid":false,"given":"Md Mahibul","family":"Hasan","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5222-8811","authenticated-orcid":false,"given":"Muhammad Ather Iqbal","family":"Hussain","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5053-7493","authenticated-orcid":false,"given":"Kaniz","family":"Fatima","sequence":"additional","affiliation":[{"name":"Institute of Business Administration, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4708","DOI":"10.1016\/j.trpro.2017.05.484","article-title":"Road Traffic Accidents in India: Issues and Challenges","volume":"25","author":"Singh","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s40890-021-00118-3","article-title":"Evaluation and Spatial Analysis of Road Accidents in Bangladesh: An Emerging and Alarming Issue","volume":"7","author":"Islam","year":"2021","journal-title":"Transp. Dev. Econ."},{"key":"ref_3","unstructured":"Alam, M.S., Mahmud, S.S., and Hoque, M.S. (2011, January 22\u201324). Road accident trends in Bangladesh: A comprehensive study. Proceedings of the 4th Annual Paper Meet and 1st Civil Engineering Congress, Dhaka, Bangladesh."},{"key":"ref_4","first-page":"290","article-title":"GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran","volume":"4","author":"Shafabakhsh","year":"2017","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media. [1st ed.].","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_6","unstructured":"AbdelBaki, H.M., Hussain, K., and Gelenbe, E. (2001, January 25\u201329). A laser intensity image based automatic vehicle classification system. Proceedings of the 2001 IEEE Intelligent Transportation Systems, Oakland, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S0968-090X(00)00034-6","article-title":"Automatic vehicle classification system with range sensors","volume":"9","author":"Harlow","year":"2001","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nashashibi, F., and Bargeton, A. (2008, January 4\u20136). Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621244"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TVT.2006.883726","article-title":"Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks","volume":"55","author":"Ki","year":"2006","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shokravi, H., Bakhary, N., Heidarrezaei, M., and Petr, M. (2020). A review on vehicle classification and potential use of smart vehicle-assisted techniques. Sensors, 20.","DOI":"10.3390\/s20113274"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bhaskar, P.K., and Yong, S.-P. (2014, January 3\u20135). Image processing based vehicle detection and tracking method. Proceedings of the 2014 International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCOINS.2014.6868357"},{"key":"ref_12","first-page":"130","article-title":"Urban road traffic condition pattern recognition based on support vector machine","volume":"13","author":"Yu","year":"2013","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.1007\/s11042-019-7684-3","article-title":"A survey on indoor RGB-D semantic segmentation: From hand-crafted features to deep convolutional neural networks","volume":"79","author":"Fooladgar","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MITS.2018.2806619","article-title":"Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems","volume":"10","author":"Chang","year":"2018","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_16","unstructured":"Cai, J., Deng, J., Khokhar, M.S., and Aftab, M.U. (2018, January 14). Vehicle Classification Based on Deep Convolutional Neural Networks Model for Traffic Surveillance Systems. Proceedings of the 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"98266","DOI":"10.1109\/ACCESS.2020.2997286","article-title":"A Super-Learner Ensemble of Deep Networks for Vehicle-Type Classification","volume":"8","author":"Hedeya","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299023"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013, January 2\u20138). 3D Object Representations for Fine-Grained Categorization. Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., 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_21","doi-asserted-by":"crossref","unstructured":"Ng, L.T., Suandi, S.A., and Teoh, S.S. (2014). Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines. The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications, Springer.","DOI":"10.1007\/978-981-4585-42-2_26"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.ins.2014.10.040","article-title":"A rapid learning algorithm for vehicle classification","volume":"295","author":"Wen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_23","unstructured":"Matos, F.M.d.S., and de Souza, R.M.C.R. (2013). Hierarchical classification of vehicle images using nn with conditional adaptive distance. International Conference on Neural Information Processing, Springer."},{"key":"ref_24","first-page":"1077","article-title":"Video-based vehicle detection and classification in challenging scenarios","volume":"7","author":"Chen","year":"2014","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_25","unstructured":"Cui, Y. (2013). Research on Vehicle Recognition in Intelligent Transportation, University of Electronic Science and Technology of China."},{"key":"ref_26","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. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cao, J., Wang, W., Wang, X., Li, C., and Tang, J. (2017). End-to-End View-Aware Vehicle Classification via Progressive CNN Learning. CCF Chinese Conference on Computer Vision, Springer.","DOI":"10.1007\/978-981-10-7299-4_61"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jo, S.Y., Ahn, N., Lee, Y., and Kang, S.-J. (2018, January 12\u201315). Transfer Learning-based Vehicle Classification. Proceedings of the 2018 International SoC Design Conference (ISOCC), Daegu, Korea.","DOI":"10.1109\/ISOCC.2018.8649802"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chauhan, M.S., Singh, A., Khemka, M., Prateek, A., and Sen, R. (2019, January 4\u20137). Embedded CNN based vehicle classification and counting in non-laned road traffic. Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, Ahmedabad, India.","DOI":"10.1145\/3287098.3287118"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","article-title":"Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection","volume":"87","author":"Kim","year":"2017","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11554-017-0712-5","article-title":"Real-time vehicle type classification with deep convolutional neural networks","volume":"16","author":"Wang","year":"2017","journal-title":"J. Real-Time Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2017.09.098","article-title":"Lane marking detection via deep convolutional neural network","volume":"280","author":"Tian","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Feng, J., Wu, X., and Zhang, Y. (2018, January 8\u20139). Lane Detection Base on Deep Learning. Proceedings of the 2018 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2018.00078"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Van Gansbeke, W., De Brabandere, B., Neven, D., Proesmans, M., and Van Gool, L. (2019, January 27). End-to-end lane detection through differentiable least-squares fitting. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00119"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, L., Li, J., Zhuo, L., and Zhu, Z. (2017, January 1\u20134). Robust Vehicle Classification Based on the Combination of Deep Features and Handcrafted Features. Proceedings of the 2017 IEEE Trustcom\/BigDataSE\/ICESS, Sydney, Australia.","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.323"},{"key":"ref_36","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Codevilla, F., Santana, E., Lopez, A., and Gaidon, A. (November, January 27). Exploring the Limitations of Behavior Cloning for Autonomous Driving. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00942"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Walambe, R., Marathe, A., and Kotecha, K. (2021). Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning. Drones, 5.","DOI":"10.3390\/drones5030066"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mikoajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_41","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7\u201312). Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kieffer, B., Babaie, M., Kalra, S., and Tizhoosh, H.R. (December, January 28). Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks. Proceedings of the 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, Canada.","DOI":"10.1109\/IPTA.2017.8310149"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Goyal, M., Goyal, R., and Lall, B. (2019). Learning Activation Functions: A new paradigm of understanding Neural Networks. arXiv.","DOI":"10.1007\/978-3-030-31760-7_1"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","unstructured":"Chollet, F. (2017). Deep Learning with Python, Simon and Schuster."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_48","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. arXiv."},{"key":"ref_49","first-page":"120","article-title":"Confusion matrix-based feature selection","volume":"710","author":"Visa","year":"2011","journal-title":"MAICS"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1080\/01431168808954945","article-title":"The derivation of global estimates from a confusion matrix","volume":"9","author":"Hay","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s00138-017-0846-2","article-title":"Vehicle classification for large-scale traffic surveillance videos using Convolutional Neural Networks","volume":"28","author":"Zhuo","year":"2017","journal-title":"Mach. Vis. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1109\/TII.2018.2822828","article-title":"Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment","volume":"14","author":"Gao","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/TITS.2019.2906821","article-title":"Accurate Classification for Automatic Vehicle-Type Recognition Based on Ensemble Classifiers","volume":"21","author":"Shvai","year":"2020","journal-title":"IEEE Trans. Intell. Transp. 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