Computer Vision for Vehicle Detection: A Comprehensive Review
DOI:
https://doi.org/10.56294/dm2025873Keywords:
Computer Vision, Object Detection, Intelligent Transportation Systems, Vehicle Detection, Deep LearningAbstract
The rapid increase in vehicle numbers has exacerbated challenges in modern transportation, leading to traffic congestion, accidents, and operational inefficiencies. Intelligent Transportation Systems (ITS) leverage computer vision techniques for vehicle detection, improving safety and efficiency. This paper aims to provide a comprehensive review of vehicle detection methods in ITS. Traditional image-processing techniques, including Scale-Invariant Feature Transform (SIFT), Viola-Jones (VJ), and Histogram of Oriented Gradients (HOG), are analyzed. Additionally, modern deep learning-based approaches are examined, distinguishing between two-stage methods such as R-CNN and Fast R-CNN, and one-stage methods like YOLO and SSD. Various image acquisition techniques, including Mono-vision, Stereo-vision, Thermal/Infrared Cameras, and Bird’s Eye View, are also reviewed. The analysis highlights the evolution from handcrafted feature-based methods to deep learning techniques, demonstrating significant improvements in detection accuracy and efficiency. One-stage detectors, particularly YOLO and SSD, offer real-time performance, while two-stage methods provide higher precision. The impact of different imaging modalities on detection reliability is also discussed. Advances in deep learning and imaging techniques have significantly enhanced vehicle detection capabilities in ITS. Future research should focus on improving robustness in diverse environmental conditions and optimizing computational efficiency for real-time deployment.
References
Mohamed Elassy, Mohammed Al-Hattab, Maen Takruri, and Sufian Badawi. Intelligent transportation systems for sustainable smart cities. Transportation Engineering, 16:100252, 2024. ISSN 2666-691X. doi: https://doi.org/10.1016/j.treng.2024.100252.
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1, 2005. doi: 10.1109/CVPR.2005.177.
David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150–1157. Ieee, 1999.
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580– 587, 2014.
Ross Girshick. Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, 2015. doi: 10.1109/ICCV.2015.169.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137–1149, 2017. doi: https://doi.org/10.1109/TPAMI.2016.2577031.
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, realtime object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, 2016. doi: 10.1109/CVPR.2016.91.
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pages 21–37. Springer, 2016.
Zhengxia Zou, Zhenwei Shi, Yuhong Guo, and Jieping Ye. Object detection in 20 years: A survey. CoRR, abs/1905.05055, 2019. URL http://arxiv.org/abs/1905.05055.
Saroj K. Meher and M.N. Murty. Efficient method of moving shadow detection and vehicle classification. AEU-International Journal of Electronics and Communications, 67(8):665–670, 2013. ISSN 1434-8411.doi: https://doi.org/10.1016/j.aeue.2013.02.001.
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–I, 2001. doi: 10.1109/CVPR.2001.990517.
F. HAN. A two-stage approach to people and vehicle detection with hog-based svm. Proc. of Workshop on Performance Metrics for Intelligent Systems, 2006, 2006.
Ji qing Luo, Hu sheng Fang, Fa ming Shao, Yue Zhong, and Xia Hua. Multi-scale traffic vehicle detection based on faster r–cnn with nas optimization and feature enrichment. Defence Technology, 17(4):1542–1554, 2021. ISSN 2214-9147. doi: https://doi.org/10.1016/j.dt.2020.10.006.
Youcef Djenouri, Asma Belhadi, Gautam Srivastava, Djamel Djenouri, and Jerry Chun-Wei Lin. Vehicle detection using improved region convolution neural network for accident prevention in smart roads. Pattern Recognition Letters, 158:42–47, 2022. ISSN 0167-8655. doi: https://doi.org/10.1016/j.patrec.2022.04.012.
Li Kang, Zhiwei Lu, Lingyu Meng, and Zhijian Gao. Yolo-fa: Type-1 fuzzy attention based yolo detector for vehicle detection. Expert Systems with Applications, 237:121209, 2024. ISSN 0957-4174.doi:https://doi.org/10.1016/j.eswa.2023.121209.
Hao Pan, Shaopeng Guan, and Xiaoyan Zhao. Lvd-yolo: An efficient lightweight vehicle detection model for intelligent transportation systems. Image and Vision Computing, 151:105276, 2024. ISSN 0262-8856. doi: https://doi.org/10.1016/j.imavis.2024.105276.
Yuhang Liu, Zhenghua Huang, Qiong Song, and Kun Bai. Pv-yolo: A lightweight pedestrian and vehicle detection model based on improved yolov8. Digital Signal Processing, 156:104857, 2025. ISSN 1051-2004. doi: https://doi.org/10.1016/j.dsp.2024.104857.
Zhichao Chen, Haoqi Guo, Jie Yang, Haining Jiao, Zhicheng Feng, Lifang Chen, and Tao Gao. Fast vehicle detection algorithm in traffic scene based on improved ssd. Measurement, 201: 111655, 2022. ISSN 0263-2241. doi: https://doi.org/10.1016/j.measurement.2022.111655.
Qiang Zhang and Yuguang Fu. Effective traffic density recognition based on resnet-ssd with feature fusion and attention mechanism in normal intersection scenes. Expert Systems with Applications, 261:125508, 2025. ISSN 0957-4174. doi: https://doi.org/10.1016/j.eswa.2024.125508.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2023.
Zaiming Sun, Chang’an Liu, Hongquan Qu, and Guangda Xie. A novel effective vehicle detection method based on swin transformer in hazy scenes. Mathematics, 10(13), 2022. ISSN 2227-7390. doi: 10.3390/math10132199.
Lili Zhang, Kang Yang, Yucheng Han, Jing Li, Wei Wei, Hongxin Tan, Pei Yu, Ke Zhang, and Xudong Yang. Tsd-detr: A lightweight real-time detection transformer of traffic sign detection for long-range perception of autonomous driving. Engineering Applications of Artificial Intelligence,139:109536, 2025. ISSN 0952-1976. doi: https://doi.org/10.1016/j.engappai.2024.109536.
Jingyuan Lei Di Tian, Jiabo Li. Multi-sensor information fusion in internet of vehicles based on deep learning: A review. Neurocomputing, 2025. doi: https://www.sciencedirect.com/science/ article/abs/pii/S0925231224016576.
Luciano Alonso Rentería Manuel Ibarra Arenado, Juan Maria Pérez Oria Carlos TorreFerrero. Monovision-based vehicle detection, distance and relative speed measurement in urban traffic. IET Intelligent Transport Systems, 2013. doi: https://ietresearch.onlinelibrary.wiley. com/doi/pdf/10.1049/iet-its.2013.0098.
Dongsheng Bao and Peikang Wang. Vehicle distance detection based on monocular vision. In 2016 International Conference on Progress in Informatics and Computing (PIC), pages 187– 191, 2016. doi: 10.1109/PIC.2016.7949492.
Tianliang Lin Zhongshen Li Yu Yao Chunhui Zhang Ronghua Ma Zhen Fang, Qihuai Chen, Shengjie Fu, and andHaoling Ren. Automatic walking method of construction machinery based on binocular camera environment perception. Micromachines, 2022. doi: https://doi.org/10.3390/ mi13050671.
Zidong Han, Junyu Liang, and Jianbang Li. Design of intelligent road recognition and warning system for vehicles based on binocular vision. IEEE Access, 6:62880–62889, 2018. doi: 10.1109/ ACCESS.2018.2876702.
Meng Ding, Xu Zhang, Wen-Hua Chen, Li Wei, and Yun-Feng Cao. Thermal infrared pedestrian tracking via fusion of features in driving assistance system of intelligent vehicles. AerospaceEngineering, 2019. doi: https://journals.sagepub.com/doi/ 10.1177/0954410019890820.
Yuanlong Wang, Hengtao Jiang, Guanying Chen, Tong Zhang, Jiaqing Zhou, Zezheng Qing, Chun-yan Wang, and Wanzhong Zhao. Efficient and robust multi-camera 3d object detection in bird-eye-view. Image and Vision Computing, 2025. doi: https://journals.sagepub.com/doi/10. 1177/0954410019890820.
Dongsuk Kum c Elijah S. Lee a, Wongun Choi b. Bird’s eye view localization of surrounding vehicles: Longitudinal and lateral distance estimation with partial appearance. Robotics and Autonomous Systems, 2019. doi: https://journals.sagepub.com/ doi/10.1177/0954410019890820.
Jingyue Shi Li Zhuo Junhui Zhao. Bev perception for autonomous driving: State of the art and future perspectives. Expert Systems With Applications, 2024. doi: https://www.sciencedirect.com/science/ article/pii/S0957417424019705?via%3Dihub.
Masato Misumi andToshiyuki Nakamiya Yoichiro Iwasaki *. Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring. sensors, 2013. doi: https://www.mdpi.com/1424-8220/13/6/7756.
T.H. Maiman. Stimulated Optical Radiation in Ruby. , 187(4736):493–494, August 1960. doi: https://doi.org/10.1038/187493a0.
Jingmeng Zhou. A review of lidar sensor technologies for perception in automated driving. Academic Journal of Science and Technology, 3(3):255–261, Nov. 2022. doi: https://doi.org/10.54097/ajst. v3i3.2993.
Tao Yang, Wei Yan, Jiancheng Lai, Yan Zhao, Zhixiang Wu, Yunjing Ji, Chunyong Wang, and Zhenhua Li. Ranging accuracy difference correction among channels of a vehicle-borne road detection lidar. Optics & Laser Technology, 174:110477, 2024. ISSN 0030-3992. doi:https://doi.org/10.1016/j.optlastec.2023.110477. URL:https://www.sciencedirect.com/ science/article/pii/S0030399223013701.
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Copyright (c) 2025 Soufiane El Asri , Khalid Zebbara, Mohammed Aftatah, Abderrahmane Azaz, Abderrahmane Ait Lhoussaine, Karim Ait Sidi Lahcen, Mohamed Baarar, Oussama Boubrine (Author)
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