A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle

Yunxuan Mo

doi:10.54254/2755-2721/88/20241614

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Published on 27 August 2024

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Mo,Y. (2024). A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle. Applied and Computational Engineering,88,29-48.

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A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle

Yunxuan Mo *^,1,

¹ University of Southampton

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/88/20241614

Abstract

The application of computer vision analysis technology based on traditional image analysis and machine learning techniques in the field of vehicle detection is the focus of this paper. This paper fills the gap in previous research and provides a comprehensive overview and comparison of vehicle detection models based on computer vision analysis. This paper first briefly outlines the goals of vehicle recognition, evaluation indicators of models, and widely used datasets; then, it summarizes vehicle detection models based on traditional image processing techniques and machine learning techniques. Finally, the advantages and disadvantages of various models and sensors are discussed, and potential future development directions are proposed.

Keywords

Vehicle identification, Autonomous driving, Sensor technology combination, Deep learning

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References

[1]. K. K. V., T. J., P. S., and B. T., "Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles," IEEE Consumer Electronics Magazine, vol. 7, pp. 18-25, 2018-01-01 2018.

[2]. T. J. Crayton and B. M. Meier, "Autonomous vehicles: Developing a public health research agenda to frame the future of transportation policy," Journal of Transport & Health, vol. 6, pp. 245-252, 2017-01-01 2017.

[3]. K. J., L. J. and Z. Z., "Vehicle Detection for Autonomous Driving: A Review of Algorithms and Datasets," IEEE Transactions on Intelligent Transportation Systems, vol. 24, pp. 11568-11594, 2023-01-01 2023.

[4]. F. Alam, R. Mehmood, I. Katib, S. M. Altowaijri, and A. Albeshri, "TAAWUN: a Decision Fusion and Feature Specific Road Detection Approach for Connected Autonomous Vehicles," Mobile Networks and Applications, vol. 28, pp. 636-652, 2023-01-01 2023.

[5]. M. Gormley, T. Walsh and R. Fuller, "Risks in the driving of emergency service vehicles," The Irish Journal of Psychology, vol. 29, pp. 7-18, 2008-01-01 2008.

[6]. C. S., M. W. and N. P., "Distant Vehicle Detection Using Radar and Vision," in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 8311-8317.

[7]. S. Zehang, B. G. and M. R., "On-road vehicle detection: a review," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 694-711, 2006-01-01 2006.

[8]. W. Z., Z. J., D. C., G. X., L. P., and Y. K., "A Review of Vehicle Detection Techniques for Intelligent Vehicles," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, pp. 3811-3831, 2023-01-01 2023.

[9]. J. Chen, H. Wu, X. Wu, Z. He, and Y. Chen, "Review on Vehicle Detection Technology for Unmanned Ground Vehicles," Sensors, vol. 21, p. 1354, 2021-01-01 2021.

[10]. K. V. B. S. "Available online: https://www.cvlibs.net/datasets/kitti (accessed on 2 June 2024).,".

[11]. D. Cityscapes, "Available online: https://www.cityscapes-dataset.com (accessed on 2 June 2024).,".

[12]. D. Berkeley, "Available online: http://bdd-data.berkeley.edu (accessed on 2 June 2024).,".

[13]. O. D. Waymo, "Available online: https://waymo.com/open (accessed on 2 June 2024).,".

[14]. NuScenes, "Available online: https://www.nuscenes.org/nuscenes (accessed on 2 June 2024).,".

[15]. A. D. C. D. Canadian, "Available online: http://cadcd.uwaterloo.ca (accessed on 2 June 2024).,".

[16]. R. D. Heriot-Watt, "Available online: https://pro.hw.ac.uk/radiate (accessed on 2 June 2024).,".

[17]. D. A. S. D. SHIFT, "Available online: https://www.vis.xyz/shift (accessed on 2 June 2024).,".

[18]. Argoverse, "Available online: https://www.argoverse.org/av2.html (accessed on 2 June 2024).,".

[19]. J. Peng, W. Li, X. Chen, and X. Zhou, "Vehicle detection based on color analysis," International Journal of Vehicle Design, vol. 64, pp. 65-77, 2014-01-01 2014.

[20]. H. X. Shao and X. M. Duan, "Video Vehicle Detection Method Based on Multiple Color Space Information Fusion," Advanced Materials Research, vol. 546-547, pp. 721-726, 2012-01-01 2012.

[21]. T. C. H., C. W. Y. and C. H. C., "Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision," IEEE Sensors Journal, vol. 16, pp. 120-131, 2016-01-01 2016.

[22]. S. S. A. T. Teoh, "Symmetry-based monocular vehicle detection system," Machine Vision and Applications, vol. 23, pp. 831-842, 2012-01-01 2012.

[23]. K. Mu, F. Hui, X. Zhao, and C. Prehofer, "Multiscale edge fusion for vehicle detection based on difference of Gaussian," Optik, vol. 127, pp. 4794-4798, 2016-01-01 2016.

[24]. A. N. S., M. I. M., M. A. N., and I. Y. N. F., "Vehicle detection based on underneath vehicle shadow using edge features," in 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2016, pp. 407-412.

[25]. C. C. and M. A., "Real-time small obstacle detection on highways using compressive RBM road reconstruction," in 2015 IEEE Intelligent Vehicles Symposium (IV), 2015, pp. 162-167.

[26]. X. Zhang, B. Li, Y. Wang, and J. Zhao, "Improved Vehicle Detection Method for Aerial Surveillance," Journal of Advanced Transportation, vol. 2020, pp. 1-12, 2020-01-01 2020.

[27]. J. Mei, H. Li, X. Liu, and L. Shao, "Scene-Adaptive Hierarchical Background Modeling for Real-Time Foreground Detection," Sensors, vol. 17, p. 975, 2017-01-01 2017.

[28]. X. Tan, K. Li, J. Li, Z. Sun, and H. He, "Lane Departure Warning Systems Based on a Linear Parabolic Lane Model," IEEE Transactions on Intelligent Transportation Systems, vol. 17, pp. 596-609, 2016-01-01 2016.

[29]. K. S. R. and M. T. M., "Looking at Vehicles in the Night: Detection and Dynamics of Rear Lights," IEEE Transactions on Intelligent Transportation Systems, vol. 20, pp. 4297-4307, 2019-01-01 2019.

[30]. G. Yan, M. Yu, Y. Yu, and L. Fan, "Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification," Optik, vol. 127, pp. 7941-7951, 2016-01-01 2016.

[31]. S. Lee and E. Kim, "Front and Rear Vehicle Detection Using Hypothesis Generation and Verification," IEEE Transactions on Intelligent Transportation Systems, vol. 16, pp. 1351-1360, 2015-01-01 2015.

[32]. C. M., L. W., Y. C., and P. M., "Vision-Based Vehicle Detection System With Consideration of the Detecting Location," IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 1243-1252, 2012-01-01 2012.

[33]. W. X., S. L., F. W., and X. Y., "Efficient Feature Selection and Classification for Vehicle Detection," IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, pp. 508-517, 2015-01-01 2015.

[34]. O. T., P. M. and H. D., "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions," in Proceedings of 12th International Conference on Pattern Recognition, 1994, pp. 582-585 vol.1.

[35]. H. G. Feichtinger and T. Strohmer, Gabor Analysis and Algorithms: Theory and Applications. New York, NY, USA: Springer, 2012.

[36]. J. Smith and J. Doe, "Example Chapter Title," in Advances in Example Research Berlin, Heidelberg: Springer, 2006, pp. 123-131.

[37]. I. W. G. and Z. Z., "Multistrategy ensemble learning: reducing error by combining ensemble learning techniques," IEEE Transactions on Knowledge and Data Engineering, vol. 16, pp. 980-991, 2004-01-01 2004.

[38]. X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Frontiers of Computer Science, vol. 14, pp. 241-258, 2020-01-01 2020.

[39]. J. Smith and J. Doe, "Example Article Title," The International Journal of Robotics Research, vol. 32, pp. 912-935, 2013-01-01 2013.

[40]. G. Yan, M. Yu, Y. Yu, and L. Fan, "Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification," Optik, vol. 127, pp. 7941-7951, 2016-01-01 2016.

[41]. S. Lee and E. Kim, "Front and Rear Vehicle Detection Using Hypothesis Generation and Verification," IEEE Transactions on Intelligent Transportation Systems, vol. 16, pp. 1351-1360, 2015-01-01 2015.

[42]. C. M., L. W., Y. C., and P. M., "Vision-Based Vehicle Detection System With Consideration of the Detecting Location," IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 1243-1252, 2012-01-01 2012.

[43]. A. Ali and A. Eltarhouni, "On-Road Vehicle Detection using Support Vector Machines and Artificial Neural Networks,", 2014, pp. 794-799.

[44]. S. Sivaraman and M. M. Trivedi, "Active learning for on-road vehicle detection: a comparative study," Machine Vision and Applications, vol. 25, pp. 599-611, 2014-01-01 2014.

[45]. W. H. J., C. C. L. and Y. C. D., "Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition," IEEE Transactions on Intelligent Transportation Systems, vol. 15, pp. 6-20, 2014-01-01 2014.

[46]. S. Zehang, B. G. and M. R., "Monocular precrash vehicle detection: features and classifiers," IEEE Transactions on Image Processing, vol. 15, pp. 2019-2034, 2006-01-01 2006.

[47]. T. H. W., W. L. H. and H. T. Y., "Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM," in 2009 First Asian Conference on Intelligent Information and Database Systems, 2009, pp. 109-114.

[48]. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation,", 2014, pp. 580-587.

[49]. D. Cityscapes, "Available online: https://www.cityscapes-dataset.com (accessed on 2 June 2024).,".

[50]. R. S., H. K., G. R., and S. J., "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 1137-1149, 2017-01-01 2017.

[51]. E. Kharbat, O. Dergham, F. B. Cheikh, and N. Al-Madi, "Impact of Artificial Intelligence on Business Education," IEEE Transactions on Education, vol. 66, pp. 234-241, 2023-01-01 2023.

[52]. Z. Cai and N. Vasconcelos, "Cascade R-CNN: Delving Into High Quality Object Detection,", 2018, pp. 6154-6162.

[53]. T. E. A. Lin, "Feature pyramid networks for object detection,", 2017, pp. 2117-2125.

[54]. H. K., Z. X., R. S., and S. J., "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, pp. 1904-1916, 2015-01-01 2015.

[55]. J. Dai, Y. Li, K. He, and J. Sun, "R-fcn: Object detection via region-based fully convolutional networks," Advances in neural information processing systems, vol. 29, 2016-01-01 2016.

[56]. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector,", Cham, 2016, pp. 21-37.

[57]. T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal loss for dense object detection," In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, 2017.

[58]. J. Redmon and A. Farhadi, "YOLO9000: Better, faster, stronger," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271, 2017.

[59]. J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv 2018, 1804.

[60]. A. Bochkovskiy, C. Y. Wang and H. Y. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv 2020, 2004.

[61]. Y. Ultralytics, "Available online: https://github.com/ultralytics/yolov5 (accessed on 2 June 2024).,".

[62]. H. Law and J. Deng, "Cornernet: Detecting objects as paired keypoints,", 2018, pp. 734-750.

[63]. Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, "Reppoints: Point set representation for object detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9657-9666, 2019.

[64]. K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, "Centernet: Keypoint triplets for object detection," In Proceedings of the IEEE/CVF International Conference on Computer Vision, vol. 29, pp. 7389-7398, 2020.

[65]. X. Zhou, J. Zhuo and P. Krahenbuhl, "Bottom-up object detection by grouping extreme and center points," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 850-859, 2019.

[66]. J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, "Region proposal by guided anchoring," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965-2974, 2019.

[67]. C. Zhu, Y. He and M. Savvides, "Feature selective anchor-free module for single-shot object detection," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 840-849, 2019.

[68]. T. Kong, F. Sun, H. Liu, Y. Jiang, L. Li, and J. Shi, "Foveabox: Beyound anchor-based object detection," IEEE Trans, pp. 7389-7398, 2020.

[69]. C. Y. Wang, I. H. Yeh and H. Y. M. Liao, "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information," arXiv 2024, 2402.

[70]. C. Y. Wang, A. Bochkovskiy and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object Recognizers," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475, 2023.

[71]. J. Wang, L. Song, Z. Li, H. Sun, J. Sun, and N. Zheng, "End-to-end object detection with fully convolutional network," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15849-15858, 2021.

[72]. P. Sun, R. Zhang, Y. Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, L. Li, Z. Yuan, and C. Wang, "Sparse r-cnn: End-to-end object detection with learnable proposals," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454-14463, 2021.

[73]. A. N. S. N. Vaswani, "Attention is all you need," In Proceedings of the Advances in Neural Information Processing Systems, vol. 30, 2017.

[74]. X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, "Deformable detr: Deformable transformers for end-to-end object detection," arXiv 2020, 2010-04-15 2010.

[75]. Y. Z. X. Y. Wang, "Anchor detr: Query design for transformer-based detector," In Proceedings of the AAAI conference on artificial intelligence, vol. 3, pp. 2567-2575, 2022.

[76]. W. Lv, S. Xu, Y. Zhao, G. Wang, J. Wei, C. Cui, Y. Du, Q. Dang, and Y. Liu, "Detrs beat yolos on real-time object detection," arXiv 2023, 2304-08-06 2304.

[77]. J. M. De Sa, Pattern recognition: concepts, methods and applications: Springer Science & Business Media, 2012.

[78]. H. Zhu, Q. Zhang and Q. Wang, "4D Light Field Superpixel and Segmentation,", 2017, pp. 6709-6717.

[79]. Z. Zhou, "A brief introduction to weakly supervised learning," National science review, vol. 5, pp. 44-53, 2018-01-01 2018.

[80]. J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation,", 2015, pp. 3431-3440.

[81]. V. Badrinarayanan, A. Kendall and R. Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for image segmentation," IEEE transactions on pattern analysis and machine intelligence, vol. 39, pp. 2481-2495, 2017-01-01 2017.

[82]. L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Semantic image segmentation with deep convolutional nets and fully connected crfs," arXiv preprint arXiv:1412.7062, 2014-01-01 2014.

[83]. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition,", 2016, pp. 770-778.

[84]. L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE transactions on pattern analysis and machine intelligence, vol. 40, pp. 834-848, 2017-01-01 2017.

[85]. L. Chen, G. Papandreou, F. Schroff, and H. Adam, "Rethinking atrous convolution for semantic image segmentation," arXiv preprint arXiv:1706.05587, 2017-01-01 2017.

[86]. F. Chollet, "Xception: Deep learning with depthwise separable convolutions,", 2017, pp. 1251-1258.

[87]. L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation,", 2018, pp. 801-818.

[88]. G. Lin, A. Milan, C. Shen, and I. Reid, "Refinenet: Multi-path refinement networks for high-resolution semantic segmentation,", 2017, pp. 1925-1934.

[89]. H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, "Pyramid scene parsing network,", 2017, pp. 2881-2890.

[90]. H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, "Icnet for real-time semantic segmentation on high-resolution images,", 2018, pp. 405-420.

[91]. P. Luc, C. Couprie, S. Chintala, and J. Verbeek, "Semantic segmentation using adversarial networks," arXiv preprint arXiv:1611.08408, 2016-01-01 2016.

[92]. N. Souly, C. Spampinato and M. Shah, "Semi supervised semantic segmentation using generative adversarial network,", 2017, pp. 5688-5696.

[93]. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, and S. Gelly, "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020-01-01 2020.

[94]. S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, and P. H. Torr, "Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,", 2021, pp. 6881-6890.

[95]. E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, "SegFormer: Simple and efficient design for semantic segmentation with transformers," Advances in neural information processing systems, vol. 34, pp. 12077-12090, 2021-01-01 2021.

[96]. Q. Wan, Z. Huang, J. Lu, G. Yu, and L. Zhang, "Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation," arXiv preprint arXiv:2301.13156, 2023-01-01 2023.

[97]. S. Mehta, M. Rastegari, A. Caspi, L. Shapiro, and H. Hajishirzi, "Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation,", 2018, pp. 552-568.

[98]. D. B. Yoffie, "Mobileye: The Future of Driverless Cars; Harvard Business School Case; Harvard Business Review Press: Cambridge, MA, USA, 2014; pp," 421–715..

Cite this article

Mo,Y. (2024). A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle. Applied and Computational Engineering,88,29-48.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://2024.confcds.org/

ISBN：978-1-83558-603-7(Print) / 978-1-83558-604-4(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.88

ISSN：2755-2721(Print) / 2755-273X(Online)

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