Vehicle Density Detection Using Dynamic Contour UNET Segmentation
Keywords:
Data Mining Techniques, Image Denoising, Image Segmentation, Vehicle Density, vehicle type detection.Abstract
There needs to be an improvement in traffic management systems since the fast expansion of cities has increased the number of vehicles on the road. Improving the precision and consistency of vehicle density and vehicle type recognition is the primary goal of this study, which employs state-of-the-art data mining approaches in conjunction with picture denoising and segmentation methods. The suggested technique integrates dynamic contour UNET segmentation for accurate object detection with Non-Local Wavelet Wiener Denoising for picture improvement. At the outset, we denoise the photos using the cutting-edge Non-Local Wavelet Wiener Denoising method, which manages to extract useful information from the images while simultaneously eliminating noise. The input photos are improved in quality by this technique, laying a clean slate for further analysis. To isolate and identify specific automobiles in the photos, the next phase uses dynamic contour UNET segmentation for picture segmentation. Precise vehicle boundary delineation is guaranteed by the UNET architecture, which can record both global and local characteristics. With the dynamic contour mechanism, segmentation becomes more adaptable, leading to strong performance in a wide range of traffic circumstances and environmental conditions.
Downloads
References
Ameen, H. A., Mahamad, A. K. B., Zaidan, B. B., Zaidan, A. A., Saon, S., Nor, D. M., … Mohammed, A. (2019). Deep Review and Analysis of Data exchange in Vehicle-to-Vehicle Communications Systems: Coherent Taxonomy, Challenges, Motivations, Recommendations, Substantial Analysis and Future Directions. IEEE Access, 1–1. doi:10.1109/access.2019.2949130
Bilik, I., Longman, O., Villeval, S., & Tabrikian, J. (2019). The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions. IEEE Signal Processing Magazine, 36(5), 20–31. doi:10.1109/msp.2019.2926573
Boukoberine, M. N., Zhou, Z., & Benbouzid, M. (2019). A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects. Applied Energy, 255, 113823. doi:10.1016/j.apenergy.2019.113823
Dai, X., & Wei, X. (2018). HybridNet: A fast vehicle detection system for autonomous driving. Signal Processing: Image Communication. doi:10.1016/j.image.2018.09.002
Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020). Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues. IEEE Computer Graphics and Applications, 1–1. doi:10.1109/ojcs.2020.2992630
Fatemidokht, H., Rafsanjani, M. K., Gupta, B. B., & Hsu, C.-H. (2021). Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4757–4769. doi:10.1109/tits.2020.3041746
Kuma, R., Weill, E., Aghdasi, F., & Sriram, P. (2019). Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding. 2019 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2019.8852059
Kumar, S., Singh, K., Kumar, S., Kaiwartya, O., Cao, Y., & Zhou, H. (2019). Delimitated Anti Jammer Scheme for Internet of Vehicle: Machine Learning based Security Approach. IEEE Access, 1–1. doi:10.1109/access.2019.2934632
Maes, W. H., & Steppe, K. (2018). Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends in Plant Science. doi:10.1016/j.tplants.2018.11.007
Outay, F., Mengash, H. A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transportation Research Part A: Policy and Practice, 141, 116–129. doi:10.1016/j.tra.2020.09.018
Qayyum, A., Usama, M., Qadir, J., & Al-Fuqaha, A. (2020). Securing Future Autonomous & Connected Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward. IEEE Communications Surveys & Tutorials, 1–1. doi:10.1109/comst.2020.2975048
Sakhare, K. V., Tewari, T., & Vyas, V. (2019). Review of Vehicle Detection Systems in Advanced Driver Assistant Systems. Archives of Computational Methods in Engineering. doi:10.1007/s11831-019-09321-3
Singh, P. K., Nandi, S. K., & Nandi, S. (2019). A tutorial survey on vehicular communication state of the art, and future research directions. Vehicular Communications, 100164. doi:10.1016/j.vehcom.2019.100164
Song, H., Liang, H., Li, H., Dai, Z., & Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. European Transport Research Review, 11(1). doi:10.1186/s12544-019-0390-4
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2019). Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches. Proceedings of the IEEE, 1–16. doi:10.1109/jproc.2019.2954595
Verfuss, U. K., Aniceto, A. S., Harris, D. V., Gillespie, D., Fielding, S., Jiménez, G., … Wyatt, R. (2019). A review of unmanned vehicles for the detection and monitoring of marine fauna. Marine Pollution Bulletin, 140, 17–29. doi:10.1016/j.marpolbul.2019.01.009
Xing, Y., Lv, C., & Cao, D. (2019). Personalized Vehicle Trajectory Prediction Based on Joint Time Series Modeling for Connected Vehicles. IEEE Transactions on Vehicular Technology, 1–1. doi:10.1109/tvt.2019.2960110
Xiong, R., Sun, W., Yu, Q., & Sun, F. (2020). Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles. Applied Energy, 279, 115855. doi:10.1016/j.apenergy.2020.115855
Zeadally, S., Javed, M. A., & Hamida, E. B. (2020). Vehicular Communications for ITS: Standardization and Challenges. IEEE Communications Standards Magazine, 4(1), 11–17. doi:10.1109/mcomstd.001.1900044
Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., & Wu, D. (2019). Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation Research Part C: Emerging Technologies, 100, 68–87. doi:10.1016/j.trc.2019.01.007
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
