The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
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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“In comparison to these two approaches, recent DL-based solutions such as [2], [3], [23], [24] have been widely adopted due to their robustness and improved vehicle detection performance in images and videos.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [23]
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Cited by
Sorted by citations
Items 301–328 of 328
Enhancing Vehicle Type Classification in Rare Scenarios with Consistency-Aware Generation
Wei, Z., Wei, G., & Zheng, X. (2025) · 2025 10th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE) · doi:10.1109/isaeece66033.2025.11160026
Axle Type Recognition System for Moving Vehicle Based on Object Detection
Li, Y., & Liu, X.-Y. (2025) · Chinese Control and Decision Conference · doi:10.1109/ccdc65474.2025.11090644
“Reference [7] proposed a novel framework of vehicle type classification from vehicle frontal view images using a convolutional neural network.”
Yuan, Q., Raza, M., Salem, S., Kwon, H., Hussain, J., Gu, Y., & Al-antari, M. A. (2025) · 2025 3rd International Conference on Intelligent Perception and Computer Vision (CIPCV) · doi:10.1109/cipcv65863.2025.00017
“First, we merge two public datasets in class level to mimic the real-word problems: Bit-Vehicle [24] and UA-DETRAC.”
A Method to Improve the Accuracy of Image Classification for Small Sample Vehicle Types
Wei, Z., Wei, G., & Zheng, X. (2025) · 2025 International Conference on Image and Video Processing (ICIVP) · doi:10.1109/icivp66296.2025.00027
“For example, some studies have constructed different DNN architectures, such as AlexNet, VGGNet, ResNet, etc., to extract features from vehicle images and use softmax classifiers to recognize vehicle types [5-6].”
Saranya, N. N., Ashokkumar, S., Kumar, S. M., Kumaresan, S., & Sugumaran, D. (2024) · 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM) · doi:10.1109/icaccm61117.2024.11059051
“During the last couple of years, tremendous progress has been made in machine learning thanks to the opportunities anger large quantity of labeled data[1].”
Video Defogging with a Sample-Based Defogging Approach
Kaur, N., Sharma, K., & Jain, A. (2024) · International Conferences on Contemporary Computing and Informatics · doi:10.1109/ic3i61595.2024.10829228
Parking lot vehicle recognition system
He, S., Xie, L., Huang, Y.-Z., Wang, H.-X., & Sun, X.-Y. (2024) · International Conference on Computer Vision and Pattern Analysis · doi:10.1117/12.3037817
Optimizing Vehicle Detection and Tracking Efficiency Through YOLO-Based Multi-Objective Approach
Abbas, R. F., & Abdulmunim, M. E. (2024) · Journal of Al-Qadisiyah for Computer Science and Mathematics · doi:10.29304/jqcsm.2024.16.21543
“Including annotated cases with varying sizes improves the accuracy of detecting small vehicle objects [26].”
BackgroundComparative Study on Real-Time Vehicle Classification
Sharma, D., Pradhan, R., & Agrawal, R. (2022) · SMART · doi:10.1109/smart55829.2022.10046885
“[2] recommended a Convolutional based Neural Network model which is semi-supervised and performs the classification of vehicles by taking their frontal images as input.”
MethodBouzi, W., Bentaieb, S., & Ouamri, A. (2022) · EDIS · doi:10.1109/edis57230.2022.9996516
“The experimental aspect of our suggested system uses the BIT Vehicles dataset [4], employed to investigate the classification performance of dilated multi-scale CNN for vehicle type classification.”
MethodInfluentialVehicle Detection based on Positive Samples Selection with Low Threshold
Sang, J., Qiao, X., Wu, Z., Tian, S., & Xia, X.-F. (2022) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/smc53654.2022.9945360
“The experiments were conducted on BIT-Vehicle [30] public dataset provided by Beijing Institute of Technology.”
MethodVehicle type classification under traffic monitoring based on improved capsule network
Yu, X., Zhang, W., Wang, Y., Huang, Z., Hong, X., & Jiang, B. (2022) · Other Conferences · doi:10.1117/12.2636610
Ma, N., & Wen, T. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.06.009
A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review
Yusup, R. A., Wang, H.-H., & Lin, W.-B. (2021) · International Journal on Advanced Science, Engineering and Information Technology · doi:10.18517/ijaseit.10.1.10195
“A paper implemented a semi-supervised convolutional neural network for vehicle type recognition where the SoftMax classifier function as the output layer, and its filters are studied using the Laplacian filter learning approach [17].”
MethodAutonomous motor vehicle categorisation using a convolutional neural network
Cassar, C. P., Gatt, T., & Briffa, I. (2020) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp51029.2020.9266135
“Dong, Wu, Pei and Jia (2015) [6] proposed a vehicle categorisation system using a semi-supervised CNN from vehicle frontal view images.”
BackgroundPerceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning
Nidamanuri, J., Karri, A. P., & Venkataraman, H. (2020) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acit49673.2020.9208871 · Cited by 2
Wireless Location Algorithm of Digital Broadcast Signals Based on Multi-Convolution Neural Network
Zhang, X.-P., Qiu, M., & Qu, F.-C. (2020) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1574/1/012051
RetractedR, N., Stephen, J., Nandakumar, N., & T, R. (2020) · International Conference Computing Methodologies and Communication · doi:10.1109/iccmc48092.2020.iccmc-000129
Targets Recognition Based on Deep Learning
Liu, H., Kuang, L., & Liu, Q.-H. (2019) · Progress in Electromagnetics Research Symposium · doi:10.1109/piers-fall48861.2019.9021700
From simulation to reality: ground vehicle detection in aerial imagery based on deep learning
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“Due to the high mobility and low cost of UAV, it is worth collecting traffic data through UAV.”
BackgroundInfluentialCharacterizing the SOM Feature Detectors Under Various Input Conditions
Cordel, M. O., & Azcarraga, A. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16142-2_12 · Cited by 2
Driver Head Analysis Based on Deeply Supervised Transfer Metric Learning with Virtual Data
Liu, K., Liu, Y.-Z., Sun, Q.-S., Pranata, S., & Shen, S. (2017) · Pacific Rim Conference on Multimedia · doi:10.1007/978-3-319-77383-4_28
Vehicle type recognition based on increasing hierarchical feature convolutional neural networks
Jiang, X., Su, X., Cai, X.-S., Li, H., & Luo, Z. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery · doi:10.1109/fskd.2017.8393409
“Research on vehicle type recognition mainly focus on identification of wagon, medium cars and pony cars [1] [2], which is based on the theory of Convolutional Neural Networks (CNNs) [3].”
BackgroundAssessment of Library Users' Feedback Using Modified Multilayer Perceptron Neural Networks
Nandhakumar, K., & Christopher, T. (2017) · doi:10.21917/ijsc.2017.0209
“Implementation of a novel method for vehicle type classification by using semi-supervised convolutional neural network is proposed in [16].”
MethodMulti-Vehicle Object Recognition Method Based on YOLOv7-W
Liu, X., Zhao, J., Zhang, Z., Wu, R., & Li, X. (2025) · IEEE Access · doi:10.1109/access.2025.3569849 · Cited by 1
“Common vehicle-object datasets such as BIT-Vehicle Dataset [35], contain 9,850 vehicle images categorized into the following six types: buses, minibuses, minivans, cars, sport utility vehicles, and trucks.”
A Seismic Prediction Algorithm for Geomagnetic Anomaly Information Based on CNN
Li, S.-D., & Huang, Y. (2023) · Geomatics Science and Technology · doi:10.12677/gst.2023.113025
Object Attribute Recognition Based on Hard Negative Mining and Convolutional Neural Network
Lei, M., & Liu, F. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-3863-6_14
Internet of Things and Deep Learning
Duan, M., Li, K., & Li, K. (2020) · Scalable computing and communications · doi:10.1007/978-3-030-43795-4_6 · Cited by 1
“In the convolutional layer, convolutions which are performed between the previous layer and a series of filters, extract features from the input feature maps [8, 54].”
Background
Metadata
- Authors
- Dong, Z., Wu, Y., Pei, M., & Jia, Y.
- Affiliations
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2015-08
- Volume
- 16
- Issue
- 4
- Pages
- 2247-2256
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- 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
- 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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