EMVD: Efficient Multitype Vehicle Detection Algorithm Using Deep Learning Approach in Vehicular Communication Network for Radio Resource Management

Full Text (PDF, 608KB), PP.25-37

Views: 0 Downloads: 0


Vartika Agarwal 1,* Sachin Sharma 1

1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2022.02.03

Received: 10 Dec. 2021 / Revised: 6 Jan. 2022 / Accepted: 26 Feb. 2022 / Published: 8 Apr. 2022

Index Terms

FRCNN, Vehicular communication network, Radio Resource Management, Real Time Traffic Database, Vehicle to Vehicle communication.


Radio resource allocation in VCN is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user have to miss their important work. In this paper, we propose radio resource allocation scheme so that user can utilize their time by taking the advantage of subscription plan. In this scenario, multitype vehicle identification scheme from real time traffic database is proposed, its history will match in transport database and vehicle travelling history database. Proposed method indicates 95% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.

Cite This Paper

Vartika Agarwal, Sachin Sharma, " EMVD: Efficient Multitype Vehicle Detection Algorithm Using Deep Learning Approach in Vehicular Communication Network for Radio Resource Management", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.2, pp. 25-37, 2022. DOI: 10.5815/ijigsp.2022.02.03


[1] Kshirsagar, P. S., & Pujar, A. M. (2017). Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing. International Journal of Modern Education and Computer Science, 9(2), 27.

[2] Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017, July). Learning to optimize: Training deep neural networks for wireless resource management. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1-6). IEEE.

[3] Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6), 1137-1149.

[4] Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., & Xue, X. (2017, July). Evolving boxes for fast vehicle detection. In 2017 IEEE international conference on multimedia and Expo (ICME) (pp. 1135-1140). IEEE.

[5] Espinosa, J. E., Velastin, S. A., & Branch, J. W. (2017, November). Vehicle detection using alex net and faster R-CNN deep learning models: a comparative study. In International Visual Informatics Conference (pp. 3-15). Springer, Cham.

[6] Eggert, C., Brehm, S., Winschel, A., Zecha, D., & Lienhart, R. (2017, July). A closer look: Small object detection in faster R- CNN. In 2017 IEEE international conference on multimedia and expo (ICME) (pp. 421-426). IEEE.

[7] Roh, M. C., & Lee, J. Y. (2017, May). Refining faster-RCNN for accurate object detection. In 2017 fifteenth IAPR international conference on machine vision applications (MVA) (pp. 514-517). IEEE.

[8] Aggarwal, A., Verma, R., & Singh, A. (2018). An efficient approach for resource allocations using hybrid scheduling and optimization in distributed system. Int. J. Educ. Manag. Eng., 8(3), 33-42.

[9] Luo, C., Ji, J., Wang, Q., Chen, X., & Li, P. (2018). Channel state information prediction for 5G wireless communications: A deep learning approach. IEEE Transactions on Network Science and Engineering, 7(1), 227-236.

[10] Guo, J., Yang, C., & Chih-Lin, I. (2018). Exploiting future radio resources with end-to-end prediction by deep learning. IEEE Access, 6, 75729-75747.

[11] Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3339-3348).

[12] Sun, X., Wu, P., & Hoi, S. C. (2018). Face detection using deep learning: An improved faster RCNN approach. Neurocomputing, 299, 42-50.

[13] Cao, C., Wang, B., Zhang, W., Zeng, X., Yan, X., Feng, Z., ... & Wu, Z. (2019). An improved faster R-CNN for small object detection. IEEE Access, 7, 106838-106846.

[14] Ahmed, K. I., Tabassum, H., & Hossain, E. (2019). Deep learning for radio resource allocation in multi-cell networks. IEEE Network, 33(6), 188-195.

[15] Gao, J., Khandaker, M. R., Tariq, F., Wong, K. K., & Khan, R. T. (2019, September). Deep neural network based resource allocation for V2X communications. In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) (pp. 1-5). IEEE

[16] Wen, D., Li, X., Zeng, Q., Ren, J., & Huang, K. (2019). An overview of data-importance aware radio resource management for edge machine learning. Journal of Communications and Information Networks, 4(4), 1-14.

[17] He, Z., & Zhang, L. (2019). Multi-adversarial faster-RCNN for unrestricted object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6668-6677).

[18] Ap, N. P., Vigneshwaran, T., Arappradhan, M. S., & Madhanraj, R. (2020, July). Automatic Number Plate Detection in Vehicles using Faster R-CNN. In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-6). IEEE.

[19] Weihong, W., & Jiaoyang, T. (2020). Research on license plate recognition algorithms based on deep learning in complex environment. IEEE Access, 8, 91661-91675.

[20] Zhang, H., Zhang, H., Long, K., & Karagiannidis, G. K. (2020). Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation. IEEE Transactions on Network Science and Engineering, 7(4), 2406-2415.

[21] Hossain, M. S., & Muhammad, G. (2020). A deep-tree-model-based radio resource distribution for 5G networks. IEEE Wireless Communications, 27(1), 62-67.

[22] Dong, R., She, C., Hardjawana, W., Li, Y., & Vucetic, B. (2020). Deep learning for radio resource allocation with diverse quality-of-service requirements in 5g. IEEE Transactions on Wireless Communications.

[23] Shen, Y., Shi, Y., Zhang, J., & Letaief, K. B. (2020). Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis. IEEE Journal on Selected Areas in Communications, 39(1), 101-115.

[24] Liu, R., Yu, Z., Mo, D., & Cai, Y. (2020, July). An Improved Faster-RCNN Algorithm for Object Detection in Remote Sensing Images. In 2020 39th Chinese Control Conference (CCC) (pp. 7188-7192). IEEE.

[25] Liu, Y., Sun, P., Wergeles, N., & Shang, Y. (2021). A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 114602.

[26] Xiao, B., & Kang, S. C. (2021). Development of an Image Data Set of Construction Machines for Deep Learning Object Detection. Journal of Computing in Civil Engineering, 35(2), 05020005.

[27] Agarwal, V., Sharma, S., & Agarwal, P. (2021). IoT Based Smart Transport Management and Vehicle-to-Vehicle Communication System. In Computer Networks, Big Data and IoT (pp. 709-716). Springer, Singapore.

[28] Agarwal, V., & Sharma, S. (2020, December). IoT based smart transport management system. In International Conference on Advanced Informatics for Computing Research (pp. 207-216). Springer, Singapore.

[29] Agarwal, V., Sharma, S., & Bansal, G. (2021, May). Secured Scheduling Techniques of Network Resource Management in Vehicular Communication Networks. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 198-202). IEEE.

[30] Vartika Agarwal and Sachin Sharma, "Deep Learning Techniques to Improve Radio Resource Management in Vehicular Communication Network", International conference on sustainable advanced computing (ICSAC 2021),2021

[31] Ogidiaka, E., Nonyelum, O. F., & Irhebhude, M. E. (2021). Game-theoretic resource allocation algorithms for device-to-device communications in fifth generation cellular networks: a review. International Journal of Information Engineering and Electronic Business, 13(1), 44-51.