Cited by 387Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

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Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

Dong, Zhen, Wu, Yuwei, Pei, Mingtao, Jia, Yunde·2015·IEEE Transactions on Intelligent Transportation Systems

DOI 10.1109/tits.2015.2402438

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Abstract

In this paper, we propose a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images. In order to capture rich and discriminative information of vehicles, we introduce sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data. Serving as the output layer of the network, the softmax classifier is trained by multitask learning with small amounts of labeled data. For a given vehicle image, the network can provide the probability of each type to which the vehicle belongs. Unlike traditional methods by using handcrafted visual features, our method is able to automatically learn good features for the classification task. The learned features are discriminative enough to work well in complex scenes. We build the challenging BIT-Vehicle dataset, including 9850 high-resolution vehicle frontal-view images. Experimental results on our own dataset and a public dataset demonstrate the effectiveness of the proposed method.

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Video-based vehicle re-identification via channel decomposition saliency region network
Wang, Y.-F., Gong, B., Wei, Y., Ma, R., & Wang, L. (2022) · Applied intelligence (Boston) · doi:10.1007/s10489-021-03096-6 · Cited by 5
“The later released CompCars [1] and BIT-Vehicle [10] consist of more vehicle images and vehicle identities.”
Background
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Yang, T., Zhang, T., & Huang, L. (2020) · The Visual Computer · doi:10.1007/s00371-020-01901-w · Cited by 6
Vehicle Attribute Recognition for Normal Targets and Small Targets Based on Multitask Cascaded Network
Liu, F., Zhang, Y., Gong, H., Xu, K., & Cai, L. (2019) · Complexity · doi:10.1155/2019/6409630 · Cited by 5
“In the experiments of vehicle attribute recognition for the normal targets, two datasets are selected: the SYIT-Vehicle dataset and BIT-Vehicle dataset [31].”
Method
Multi-model induced network for participatory-sensing-based classification tasks in intelligent and connected transportation systems
Shi, H., Zhao, X.-B., Wan, H., Wang, H., Dong, J., Tang, K., & Liu, A.-F. (2018) · Comput. Networks · doi:10.1016/j.comnet.2018.05.030 · Cited by 5
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Yan, Z., Feng, Y., Cheng, C., Fu, J., Zhou, X., & Yuan, J. (2017) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2017.0066 · Cited by 7
A PCANet based method for Vehicle Make Recognition
Li, B., Dong, Y., Zhao, D., Wen, Z., & Yang, L. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795943 · Cited by 4
“[1] conclude that the currently image-based methods for Vehicle Type Classification roughly fall into two categories, model-based methods and appearance-based methods.”
Method
Segment-based CO2 Emission Evaluations from Passenger Cars based on Deep Learning Techniques
Niroomand, N., Bach, C., & Elser, M. (2021) · IEEE Access · doi:10.1109/access.2021.3135604 · Cited by 5
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Habib, S., & Khan, N. F. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015504 · Cited by 9
Classification of Vehicle Type on Indian Road Scene Based on Deep Learning
Arunkumar, K., Danti, A., Manjunatha, H., & Rohith, D. (2020) · International Conference on Recent Trends in Image Processing and Pattern Recognition · doi:10.1007/978-981-16-0507-9_16 · Cited by 5
Computer Vision Methods for Vehicle Detection and Tracking: A Systematic Review and Meta-Analysis
Matias, J., Pinto, F., & Couto, P. (2025) · Applied Sciences · doi:10.3390/app152212288 · Cited by 4
Cross-Granularity Network for Vehicle Make and Model Recognition
Tan, S.-H., Chuah, J. H., Chow, C., & Kanesan, J. (2025) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2025.3549217 · Cited by 4
“Since these raw handcrafted features are susceptible to external disturbances [16], [17], [18], [19], [20], recent feature-based approaches are paired with encoding schemes to boost the robustness.”
Lightweight Model Development for Forest Region Unstructured Road Recognition Based on Tightly Coupled Multisource Information
Lei, G.-N., Guan, P., Zheng, Y.-L., Zhou, J.-J., & Shen, X.-Q. (2024) · Forests · doi:10.3390/f15091559 · Cited by 4
A Multi-Modal Tensor Ring Decomposition for Communication-Efficient and Trustworthy Federated Learning for ITS in COVID-19 Scenario
Zhao, R., Yang, L. T., Liu, D., Zhou, X., Deng, X., & Tang, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3273167 · Cited by 8
“Three real-world datasets MNIST [25], BIT-Vehicle [26] and COVID-CT [27] are leveraged to evaluate the performance of the proposed TR-TSVD CNN under the FL architecture, where MNIST is to verify the correctness and effectiveness of the proposed approach for image processing that also lays a…”
Method
Vehicle Detection and Classification without Residual Calculation: Accelerating HEVC Image Decoding with Random Perturbation Injection
Beratoğlu, M. S., & Toreyin, B. U. (2023) · arXiv.org · doi:10.48550/arxiv.2305.08265 · Cited by 4
“We selected the BIT dataset [10] for our experiments due to its widespread use in previous research and the diverse set of images it offers for vehicle classification.”
Method
A vehicle classification model based on deep active learning
Wang, X., Yang, S., Xiao, Y., Zheng, X., Gao, S., & Zhou, J. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.05.009 · Cited by 4
RT-VeD
Wang, S., Lu, J., Guo, B., & Dong, Z. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539183 · Cited by 5
“Vehicle type features [8] can be distinguished by differences in appearance and silhouette, by which we lock on to vehicles of the same type as VoI.”
BackgroundSelf-citation
Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network
Zhu, Z.-J., Chen, H., Xie, S.-Y., Hu, Y., & Chang, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6548811 · Cited by 4
Vehicle type classification using graph ant colony optimizer based stack autoencoder model
Rani, B., Rao, M. V., Patra, R. K., Srinivas, K., & Madhukar, G. (2022) · Multimedia tools and applications · doi:10.1007/s11042-021-11508-5 · Cited by 4
“The vehicle face images consists of precise information for the vehicle type classification, and extracting feature vectors from the vehicle face images leads to better classification results [5, 20, 23].”
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Chen, L., Zheng, L.-J., Xia, L., Liu, W.-N., & Sun, D.-H. (2021) · doi:10.1016/j.physa.2021.126324 · Cited by 4
Vehicle Classification Based on CCTV Video Recording Using Histogram of Oriented Gradients, Local Binary Patterns, and Hierarchical Multi-SVM
Navastara, D. A., Maulana, M. F., Suciati, N., & Sarena, S. T. (2021) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/1077/1/012068 · Cited by 4
“Other studies have been 1077 (2021) 012068 IOP Publishing doi:10.1088/1757-899X/1077/1/012068 2 able to classify more than one type of vehicle but are still weak against changes in vehicle direction [5] and tolerance to lighting [6] [7].”
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Component-based feature extraction and representation schemes for vehicle make and model recognition
Lü, L., & Huang, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.049 · Cited by 8
Detection and Classification of Vehicles based on Deep Semantic Features of Faster R-CNN
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“[6] proposed semi-supervised Convolution Neural Network CNN.”
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Porto, J. V. D. A., Szemes, P. T., Pistori, H., & Menyhárt, J. (2025) · IEEE Access · doi:10.1109/access.2025.3565901 · Cited by 3
Detection and Identification of Vehicles from High-Resolution Aerial Images Using Deep Learning Approaches with the Tuned Parameters
Khan, M. Z., Hassan, S. U., Draz, H. U., Khan, M. U. G., & Iqbal, R. (2020) · Unmanned Aerial Vehicles in Smart Cities · doi:10.1007/978-3-030-38712-9_5 · Cited by 4
How does lane status influence drivers’ lane-change decisions? — An analysis based on naturalistic driving data
Zhang, H.-R., Wang, Y., Li, D., Li, J., Cao, Y.-F., Cheng, Y., & Ranjitkar, P. (2026) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.103453 · Cited by 3
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Çelik, B., Tortum, A., Çintaş, E., & Özyer, B. (2025) · Promet - Traffic&Transportation · doi:10.7307/ptt.v37i4.978 · Cited by 3
“The dataset was created with traffic images collected from the Shandong region of China and images retrieved from BIT Vehicle_Dataset (Dong et al. [31]) to train the network.”
Vehicle classification using accelerometer signals and machine-learning techniques
Pandey, A. D., Kumar, B., Parida, M., Mudgal, A., Chouksey, A., & Mishra, R. (2025) · Journal of Intelligent Transportation Systems / Taylor & Francis · doi:10.1080/15472450.2025.2497507 · Cited by 3
CL-Shield: A Continuous Learning System for Protecting User Privacy
Li, T., Wang, H.-L., Li, Q., Jiang, Y., & Yuan, Z. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3504721 · Cited by 3
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Islam, N., Afm, M., Hasan, R., Ray, S. K., Hossain, M. A., Al, M., & Shammo, Z. (2024) · 2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET) · doi:10.1109/icrpset64863.2024.10955913 · Cited by 3
“A semi-supervised convolutional neural network strategy was suggested by Dong et al. [13] for the categorization of cars based on their front views.”
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Kang, C., Lee, T., & Shin, J. (2024) · Machines · doi:10.3390/machines12080564 · Cited by 3
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Hayee, S., Hussain, F., & Yousaf, M. H. (2023) · Sensors · doi:10.3390/s23187920 · Cited by 6
Tensor-Empowered LSTM for Communication-Efficient and Privacy-Enhanced Cognitive Federated Learning in Intelligent Transportation Systems
Zhao, R., Yang, L. T., Liu, D., Lu, W., Zhu, C., & Ruan, Y. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3575661 · Cited by 4
“Two real-world datasets—BIT-Vehicle [4] and MNIST [7]—are leveraged to evaluate the performance of the proposed lightweight privacy-enhanced CFL with RB-LSTM.”
Background
Real-Time Traffic Classification through Deep Learning
Priymak, M., & Sinnott, R. (2021) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1145/3492324.3494165 · Cited by 5
“Laplacian filter learning method has been shown to be effective at vehicle type classification [5].”
Method
Enhanced Spatial–Temporal Map-Based Video Analytic Platform and Its Local- Versus Cloud-Based Deployment with Regional 511 Camera Network
Ge, Y., Jin, P., Zhang, T., & Martinez, J. (2021) · Transportation Research Record · doi:10.1177/03611981211036377 · Cited by 3
“Dong et al. ( 16 ), Mithun et al. ( 17 ), and Zhang ( 18 ) developed vehicle classification systems based on AI technology.”
Background
A Solution for Vehicle Attributes Recognition and Cross-dataset Annotation
Xi, J., Wang, Z.-H., & Fan, D. (2020) · 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei51763.2020.9263546 · Cited by 3
“For example, BIT-Vehicle [2] dataset comprises location information and the surveillance-nature data part of the CompCars [3] (sv_data) contains rich information of attributes, but the latter lacks location information.”
Background
Vehicle Type Classification System for Expressway Based on Improved Convolutional Neural Network
Zhang, Y.-B., Liu, Q., & Hao, P.-F. (2020) · 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd49809.2020.9137449 · Cited by 3
“Feature recognition and extraction of the input image data can be carried out through convolution operation, so as to achieve the purpose of image recognition [10].”
Background
An Artificial Vision Based Method for Vehicle Detection and Classification in Urban Traffic
Camacho, E. C., Pedraza, C., & Higuera, C. (2019) · Iberian Conference on Pattern Recognition and Image Analysis · doi:10.1007/978-3-030-31321-0_34 · Cited by 3
MoVITS: Moroccan Video Intelligent Transport System
Bourja, O., Kabbaj, K., Derrouz, H., Bouziady, A. E., Thami, R. O. H., Zennayi, Y., & Bourzeix, F. (2018) · Colloquium in Information Science and Technology · doi:10.1109/cist.2018.8596566 · Cited by 3
“Recently a deep learning method is proposed by Zheng [13] where the authors presented a vehicle classification method based on the deep convo-lutional neural network.”
Method
End-to-End View-Aware Vehicle Classification via Progressive CNN Learning
Cao, J., Wang, W., Wang, X., Li, C., & Tang, J. (2017) · Chinese Conference on Computer Vision · doi:10.1007/978-981-10-7299-4_61 · Cited by 3
Classification of vehicles in traffic and detection faulty vehicles by using ANN techniques
Baser, E., & Altun, Y. (2017) · 2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) · doi:10.1109/ebbt.2017.7956758 · Cited by 3
“Ayrıca Bus, Microbus, Minivan, Sedan, SUV, Truck olarak Şekil 1’de görüldüğü gibi 6 farklı kategoride araç tipi bulunmaktadır [8], [9].”
A Vehicle Classification System for Intelligent Transport System using Machine Learning in Constrained Environment
Alghamdi, A., Imran, T., Mursi, K. T., Ejaz, A., Kamran, M., & Alamri, A. M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140746 · Cited by 3
“Zhen dong et al. in [17] worked on the vehicle classification by using a semi supervised CNN.”
Method
Vehicle-Type Classification Using Customized Fuzzy Convolutional Neural Network
Mane, D., Kumbharkar, P. B., Dhotre, P. S. R., & Borde, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-0171-2_40 · Cited by 8
View-independent Vehicle Category Classification System
Baghdadi, S., & Aboutabit, N. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120786 · Cited by 3
“[18,1,6,5] used Convolutional Neural Networks to classify vehicle types.”
BackgroundMethod
Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos
S, S., & R, D. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120911 · Cited by 3
“An unsupervised convolutional neural network is used for classification of vehicle type in [13] based on the vehicle frontal images.”
Method
Classification of Trucks using Convolutional Neural Network
Lee, D.-G. (2018) · doi:10.22156/cs4smb.2018.8.6.375 · Cited by 3
“또한 대부분의 차종 분류는 차량의 전면이나 측면 영상을 사용하여 차 량의 검출이나 차종을 분류를 하고 있다[5-12].”
A Robust Vehicle Make and Model Recognition System for ITS Applications
Siddiqui, A. J. (2015) · uO Research (University of Ottawa) · doi:10.20381/ruor-4094 · Cited by 2
“…by a 2 Stage Unsupervised Convolution Neural Network (with Layer Skipping) Softmax Regression Bus, MicroBus, Minivan, SUV, Sedan, Truck Dong et al. (2015) [56] Local and Global Features generated by a 2-Stage Semi supervised Convolution Neural Network Softmax Regression Bus,…”
Method
MHP-DETR: A Vehicle Detection System Based on Multimodal Attention and Hypergraph Fusion for Complex Weather Conditions
Liu, H., Cheng, X., & Wang, C. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3608212 · Cited by 2
“2) BITVehicle Dataset : BITVehicle [23]”
YOLOv10-Based Object Detection Method with Optimized Defogging Algorithm for Autonomous Driving in Foggy Environments
Wang, Y., & Lu, Y.-F. (2024) · 2024 International Conference on Intelligent Robotics and Automatic Control (IRAC) · doi:10.1109/irac63143.2024.10871902 · Cited by 2
Hybrid Edge Detection Method for Autonomous Vehicle Navigation
Kaur, N., Sharma, K., & Jain, A. (2024) · International Conferences on Contemporary Computing and Informatics · doi:10.1109/ic3i61595.2024.10829337 · Cited by 2
A New License Plate De-Identification Method for Indoor Parking Lot Datasets
Nam, S.-H., & Park, H.-S. (2024) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-024-02033-0 · Cited by 2
“Zou [13] proposed a CNN-based license plate detection method using a multi-vehicle dataset, BIT-Vehicle [19].”
Influential
Distributed sparsity constrained optimization over the Stiefel manifold
Qu, W., Chen, H., Xiu, X.-C., & Liu, W.-Q. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128267 · Cited by 2
“Cost-Effective Vehicle Type Recognition in Surveillance Images.”
Suryawanshi, R., Jadhao, A., Ramteke, B., Ganorkar, H., Pahade, M., Bansod, A., & Dubey, R. (2024) · International Journal of Advanced Research in Science, Communication and Technology · doi:10.48175/ijarsct-18775 · Cited by 2
Visual examination of relations between known classes for deep neural network classifiers
Sobczak, Ł., Filus, K., Halama, M., & Domańska, J. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386987 · Cited by 4
“The great majority of datasets focused on vehicle images present cars (e.g. [ 12], [13]) or provide images from aerial platforms (e.g. [14], [15]).”
Background
Design of Image Recognition Monitoring System of Hydrological Monitoring Station Based on Edge Intelligence
Tan, L.-W., Yao, X., Long, Y., Zhang, Z., Li, Z.-W., & Li, Y. (2022) · 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) · doi:10.1109/icarce55724.2022.10046571 · Cited by 2
“The first is Convolutional Neural Networks (CNN) for image classification [11].”
Background
MetaCNN: A New Hybrid Deep Learning Image-based Approach for Vehicle Classification Using Transformer-like Framework
Chen, J., & Luo, R.-K. (2022) · International Conference on Computer Science and Software Engineering · doi:10.1145/3569966.3570099 · Cited by 2
“Current Vehicle Classification is enabled by the remote sensing appearance-based model [9].”
Background
A Hybrid System with Number Plate Recognition and Vehicle Type Identification for Vehicle Authentication at the Restricted Premises
Bharti, N., Kumar, M., & Manikandan, V. (2022) · 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET) · doi:10.1109/icefeet51821.2022.9847975 · Cited by 2
“The softmax regression is used to classify vehicle kinds after acquiring the ultimate characteristics [20].”
Method
Vehicle Recognition Combining ResNeXt Network and Random Erasing
Dong, Q., & Hu, C. (2022) · International Conference on Virtual Reality · doi:10.1109/icvr55215.2022.9848135 · Cited by 2
“Then they use the closest distance classification method to optimize the object pose and adaptive 978-1-6654-79 1 1-0/22/$3 1 Dong [7] put forwards a method of car model recognition from the front view image of the vehicle using a semi-supervised convolutional neural network.”
Method
Yapay Sinir Ağları, Destek Vektör Makineleri ve AdaBoost Algoritması ile Araç Sınıflandırmasının Değerlendirilmesi
Kul, S., & Sayar, A. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.1023889 · Cited by 2
Temporal-Spatial Feature Extraction Based on Convolutional Neural Networks for Travel Time Prediction
Chen, C.-H. (2021) · arXiv.org · doi:10.48550/arxiv.2111.00149 · Cited by 2
Multi-ColorGAN: Few-shot vehicle recoloring via memory-augmented networks
Zhou, W., Chen, S.-B., Xu, L.-X., & Luo, B. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103317 · Cited by 2
Deep Feature Learning Network for Vehicle Retrieval
Phung, T. T. T., Ly, N. Q., VO, T. T., & Ho, M. T. (2021) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3453800.3453804 · Cited by 1
Vehicle Detection and Classification in Traffic Images Using ConvNets With Constrained Resources
Roecker, M. N., Costa, Y., Britto, A., Oliveira, L., & Bertolini, D. (2019) · International Conference on Systems, Signals, and Image Processing · doi:10.1109/iwssip.2019.8787310 · Cited by 2
“90% of classification accuracy when the model was evaluated on a uniform subset of the BIT-Vehicle Dataset [8] with six vehicles classes: Bus, Microbus, Minivan, Sedan, SUV, and Truck.”
BackgroundMethodInfluential
An Efficient and Slight Convolutional Neural Network for Vehicle Type Classification
Zheng, H., Gu, N.-J., & Zhang, X.-C. (2018) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1069/1/012116 · Cited by 2
“Datasets The open source dataset BIT-Vehicle [15] is adopted to verify the versatility of our CNN in this paper.”
Method
Self-organizing Maps as Feature Detectors for Supervised Neural Network Pattern Recognition
Cordel, M. O., Antioquia, A. M. C., & Azcarraga, A. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46681-1_73 · Cited by 3
Towards the next generation intelligent transportation system: A vehicle detection and counting framework for undisciplined traffic conditions
Ahmed, S. H., Raza, M., Kazmi, M., Mehdi, S. S., Rehman, I., & Qazi, S. A. (2023) · Neural Network World · doi:10.14311/nnw.2023.33.011 · Cited by 3
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BackgroundMethod
End-to-End Car Make and Model Classification using Compound Scaling and Transfer Learning
Bourja, O., Maach, A., Zannouti, Z., Derrouz, H., Abdelali, H. A., Thami, R. O. H., & Bourzeix, F. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.01305111 · Cited by 2
“[18] 2015 proposed a sparse Laplacian filter learning method to minimize the parameters of convolutional layers with a large number of unlabeled samples.”
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Research on Vehicle Recognition Algorithm based on Convolution Neural Network
Zhang, P., Chen, H.-C., & Li, Q. (2021) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1865/4/042117 · Cited by 2
Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things
Mitra, R. R. ·. K., Ranjan, R., Mitra, K., Jayaraman, P., Wang, L.-Z., & Zomaya, A. (2020) · Scalable Computing and Communications · doi:10.1007/978-3-030-43795-4 · Cited by 2
Vehicle make and model recognition for intelligent transportation monitoring and surveillance.
Tafazzoli, F. (2017) · doi:10.18297/etd/2630 · Cited by 3
“The first category of methods extract appearance features [162,163], whereas in the latter, they compute the vehicle's 3D parameters such as size, silhouette dimension and aspect ratio, to recover the 3D model of the vehicle [164,165].”
Method
An enhanced traffic object detection network based on multi-point attention and sparse feature aggregation
Li, X., Wang, Q.-D., Shi, R., Nui, R., & Chen, W. (2026) · Digit. Signal Process. · doi:10.1016/j.dsp.2026.105897 · Cited by 1
Vehicle Damage Detection Using Fusion of Mixture of Experts
Cheng, K.-C., Poh, Y. Y., & Lim, T.-M. (2025) · 2025 IEEE International Conference on Computation, Big-Data and Engineering (ICCBE) · doi:10.1109/iccbe65177.2025.11255698 · Cited by 1
“While MoE is promising in managing multi-class and high-variance datasets [6], its application to vehicle damage detection remains limited, especially when for spatial cropping, class balancing, and expert specialization.”
Enhancing the capabilities of traffic surveillance system by integration of intelligent vehicle classification technique
Kumari, S., & Kumar, T. (2025) · Multimedia tools and applications · doi:10.1007/s11042-025-20921-z · Cited by 1
Efficient Roadside Vehicle Line-Pressing Identification in Intelligent Transportation Systems with Mask-Guided Attention
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End-to-end object detection method based on shape correction and feature selection cross-attention
He, L., Ge, L., Cheng, Z., Wang, X., Yao, G., & Hu, Z. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023033 · Cited by 1
Generated Compressed Domain Images to the Rescue: Cross Distillation from Compressed Domain to Pixel Domain
Keser, R., Beratoğlu, M. S., & Toreyin, B. U. (2024) · International Symposium on Circuits and Systems · doi:10.1109/iscas58744.2024.10558450 · Cited by 1
“Sample compressed and pixel domain images from license plate detection dataset, namely CD-LP [26], and vehicle classification datasets, namely BIT-Vehicle [28] and compressed BIT-Vehicle [27].”
Method
Collection and Analysis of Datasets for Low-Light Vehicle Detection
Alon-alon, J. M., Li, J. C., Dy, J. M., Ilao, J., Romblon, N., & Cempron, J. (2023) · International Conference on Mechatronics and Machine Vision in Practice · doi:10.1109/m2vip58386.2023.10413381 · Cited by 1
“Non-monitoring camera datasets include the Stanford Car Dataset [14] and The Comprehensive Cars Dataset [15].”
Method
VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Road
Lu, X., Wang, L.-F., Qi, C., Zhang, S., & Zhao, Z. (2023) · 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml60161.2023.10424891 · Cited by 1
“…dataset categorization: Compared to the first-level classification, which categorizes vehicles into small, medium, large, and extra-large classes, the second-level type can distinctly differentiate between small cars and light-duty trucks, as well as between large buses and medium-duty trucks[11].”
Background
A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit
Li, T., He, Y.-Y., Wang, M.-Q., Zhao, K., Wang, N., Gui, W., Feng, J.-H., & Yang, J. (2023) · Processes · doi:10.3390/pr11092576 · Cited by 1
A Vehicle Classification Model Based on Multi-scale Feature Fusion
Wang, X., Yang, S., Sun, Z., Li, X., & Xiao, Y. (2022) · Cybersecurity and Cyberforensics Conference · doi:10.23919/ccc55666.2022.9902422 · Cited by 1
Improving the performance of deep learning-based classification when a sample has various appearances
Jin, G., Jiao, Y., Wang, J., Ma, M., & Song, Q. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2092558 · Cited by 1
“We collected the samples from the following datasets: (Bloisi et al., 2015; Dong et al., 2015; Mash et al., 2010; Russakovsky et al., 2014; Zhang et al., 2014; Zheng et al., 2017) and the samples of frog from (https://github.com/jonshamir/frog-dataset).”
BackgroundMethod
Vehicle Type Classification Using Hybrid Features and a Deep Neural Network
Sathyanarayana, N., & Narasimhamurthy, A. (2022) · International Journal of Applied Metaheuristic Computing · doi:10.4018/ijamc.292518 · Cited by 1
“For all remaining experiments, the Gabor, HoG and LOOP features are combined and then feature selection is carried out by multi-objective Ant Colony Optimization (ACO) approach.”
MethodInfluential
DFT: A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities
Zhang, Y., Liu, Y., Yang, G., & Song, J. (2021) · Expert Systems · doi:10.1111/exsy.12916 · Cited by 1
“Dong et al. (2015) proposed a semi-supervised CNN for vehicle type classification, introducing a sparse Laplacian filter with unlabelled data and trains the classifier with small labelled images.”
BackgroundMethodInfluential
Real Time Traffic Assesment using Image Processing
Pushpalata, & Sasikala, M. (2020) · 2020 IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon50868.2020.9362387 · Cited by 1
“The authors used the BIT vehicle Dataset [7] which consists of six different classes such as SUV, Truck, Bus, Microbus, Minivan and sedan.”
Method
Verification method to improve the efficiency of traffic survey
Kang, M., Kim, P.-K., & Lim, K.-T. (2020) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas50221.2020.9268311 · Cited by 1
“Several methods [15,16] have been pro posed to improve the performance of individual steps, but in this paper, the widely used Yolo v3, Deep Feature, and ResNet methods [17] are used.”
Method
The comparison between two methods of object detection: Fast Yolo model and Delaunay Triangulation
Benjelloun, F., Elmanaa, I., Sabri, M. A., Yahyaouy, A., & Aarab, A. (2020) · International Symposium on Computer Vision · doi:10.1109/iscv49265.2020.9204197 · Cited by 1
“Dong et al. [20] have classified the vehicles by a semi supervised convolutional neural network from the front view images of the vehicles.”
Background
A Multitask Cascaded Convolutional Neural Network Based on Full Frame Histogram Equalization for Vehicle Detection
Gong, H., Zhang, Y., Xu, K., & Liu, F.-F. (2018) · ACM Cloud and Autonomic Computing Conference · doi:10.1109/cac.2018.8623562 · Cited by 1
“Most data sets used by previous vehicle detection algorithms are standard data sets, such as BIT-Vehicle dataset[19], CompCars dataset[20] KITTI dataset[21].”
Method
Towards an optimal bag-of-features representation for vehicle type classification in thermal infrared imagery
Akula, A., Ghosh, R., Guleria, N., Kumar, S., & Sardana, H. (2018) · Optical Engineering + Applications · doi:10.1117/12.2502154 · Cited by 1
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Background
Ensemble Artificial Intelligence Framework for Real-Time Automatic Vehicle Detection and Classifications
Yuan, Q., Kwon, H., Raza, M., Lee, S., Alqahtani, A., Hussain, J., Salem, S., Gu, Y., & Al-antari, M. A. (2026) · IEEE Access · doi:10.1109/access.2026.3657829 · Cited by 1
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MRTMD: A Multi-Resolution Dataset for Evaluating Object Detection in Traffic Monitoring Systems
Bugeja, M., Bartolo, M., Montebello, M., & Seychell, D. (2025) · IEEE Access · doi:10.1109/access.2025.3585986 · Cited by 1
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Visual Classification of Vehicle Types Using Transfer Learning
Jamil, M., Rehman, A., & Ahmad, R. (2026) · International Journal of Emerging Engineering and Technology · doi:10.57041/w2t5bx69
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Evaluating 2D and 3D-Aware Vision Foundation Models for Vehicle Attribute Recognition
Delazeri, A. V., Lima, G. E., Nascimento, E., Laroca, R., & Menotti, D. (2026) · doi:10.48550/arxiv.2608.29929
Smart Real-Time Tunnel Fire Risk Assessment Based on Computer Vision and a Traffic-Adaptive Correction Mechanism
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Lightweight Transfer Learning Framework for High-Accuracy Vehicle Type Classification in Intelligent Transportation Systems
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Coupled gradient-evolutionary learning in sparse memristive neuromorphic networks for robust edge intelligence
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IoT-Assisted Estimation of Vehicular Traffic Status over Wide-Area
Patel, A., & Saha, S. (2026) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets67989.2026.11418227
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Tunnel-YOLO: A New Traffic Vehicle Detection Method Using Feature Enhancement for Complex Tunnel Environments
Du, W., Xu, L.-W., Tie, X., Zhou, Y.-J., & Li, A. (2025) · Transportation Research Record · doi:10.1177/03611981251398755
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Advancing Cyclist Safety: A Robust Collision Avoidance System with DBSCAN-Enhanced Clustering and Bayesian-Optimized Adaptive Extended Kalman Filter
Thean, L. H., & Chor, W. T. (2025) · IEEE Conference on Systems, Process and Control · doi:10.1109/icspc68261.2025.11326445
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New Energy Vehicle Penetration Rate for Tunnels Based on ETC images and YOLOv8s
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“For example, the BIT-Vehicle dataset is outdated and cannot identify many new models [7] .”
Improving Fine-Grained Vehicle Classification via Multitask Learning and Hierarchical Consistency
Lima, G. E., Santos, E., Nascimento, E., Jr., Laroca, R., & Menotti, D. (2025) · SIBGRAPI Conference on Graphics, Patterns and Images · doi:10.5753/sibgrapi.est.2025.38289

Authors: Dong, Z., Wu, Y., Pei, M., & Jia, Y.
Affiliations: Beijing Institute of Technology (CN)
ORCID: Dong, Zhen
Wu, Yuwei
Pei, Mingtao
Jia, Yunde
Venue: IEEE Transactions on Intelligent Transportation Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-08
Volume: 16
Issue: 4
Pages: 2247-2256
ISSN: 1524-9050 (ISSN-L)
1558-0016
DOI: 10.1109/tits.2015.2402438
OpenAlex: W2045721916
Cited by: 387
References: 0
Citations by year: 2026: 16 · 2025: 27 · 2024: 41 · 2023: 41 · 2022: 59 · 2021: 52 · 2020: 45 · 2019: 33 · 2018: 34 · 2017: 26 · 2016: 12 · 2015: 1
FWCI: 21.13
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Funders: National Natural Science Foundation of China (61203291)
Specialized Research Fund for the Doctoral Program of Higher Education of China (20121101120029)
Specialized Fund for Joint Building Program of Beijing Municipal Education Commission
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords: vehicle classification, semi-supervised learning, convolutional neural networks, unlabeled data, multi-task learning, representation learning

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Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

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