Abstract
Road condition monitoring and control is essential for smart transportation in the era of autonomous driving. In this paper, we propose to apply unmanned aerial vehicle (UAV), wireless communications and artificial intelligence (AI) to achieve multi-object detection for smart road monitoring and control. In particular, the application of UAV enables real-time image view to monitor road condition, such as traffic flow and on-road objects, in an efficient way without disturbing normal traffic. Those raw image data are first offloaded to a road side unit through wireless communications. A computing platform connected to the road side unit can execute the AI based scheme for road condition monitoring and control. The AI based scheme is developed around convolutional neural network (CNN). For demonstration, the objects of interest considered in this work include advertisement billboards, junctions, traffic signs and unsafe objects. Other objects can be extended to the developed system with more collected data. To evaluate the proposed scheme, we launched a UAV to collect real-life road images from multiple road sections of a highway. The AI based scheme is then developed using portion of the raw data. Test of the AI scheme is conducted using the rest of the dataset. The evaluation results have demonstrated that the proposed UAV based multi-object detection scheme can provide accurate results to support efficient road condition monitoring and control in future smart transportation.
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References
Essex, A., Shinkle, D., Miller, A., Pula, K.: Traffic safety trends—state legislative action 2017. In: National Conference of State Legislatures (2018)
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Clarke, R.Y.: Smart cities and the internet of everything: the foundation for delivering next-generation citizen services. IDC Government Insights (2013)
Ćorović, A., Ilić, V., Durić, S., Marijan, M., Pavković, B.: The real-time detection of traffic participants using yolo algorithm. In: 26th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2018)
Eisenbach, M., et al.: How to get pavement distress detection ready for deep learning? A systematic approach. In: International Joint Conference on Neural Networks (IJCNN), pp. 2039–2047, May 2017. https://doi.org/10.1109/IJCNN.2017.7966101
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, June 2014. https://doi.org/10.1109/CVPR.2014.81
Goyal, R., Kumari, A., Shubham, K., Kumar, N.: IoT and XBee based smart traffic management system. J. Commun. Eng. Syst. 8(1), 8–14 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kazmi, A., Tragos, E., Serrano, M.: Underpinning IoT for road traffic noise management in smart cities. In: IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 765–769, March 2018. https://doi.org/10.1109/PERCOMW.2018.8480142
Kharchenko, V., Chyrka, I.: Detection of airplanes on the ground using YOLO neural network. In: IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET), pp. 294–297, July 2018. https://doi.org/10.1109/MMET.2018.8460392
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, vol. 1, p. 4 (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Mihelj, J., Kos, A., Sedlar, U.: Source reputation assessment in an IoT-based vehicular traffic monitoring system. Procedia Comput. Sci. 147, 295–299 (2019)
Moore, W., et al.: Transportation statistics annual report, 2016. United States, Bureau of Transportation Statistics (2017)
Nagmode, V.S., Rajbhoj, S.: An intelligent framework for vehicle traffic monitoring system using IoT. In: International Conference on Intelligent Computing and Control (I2C2), pp. 1–4. IEEE (2017)
Nakao, A., et al.: End-to-end network slicing for 5G mobile networks. J. Inf. Process. 25, 153–163 (2017)
Nwankpa, C., Ijomah, W., Gachagan, A., Marshall, S.: Activation functions: comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378 (2018)
Öhlén, P., et al.: Data plane and control architectures for 5G transport networks. J. Lightwave Technol. 34(6), 1501–1508 (2016)
Patel, R., Dabhi, V.K., Prajapati, H.B.: A survey on IoT based road traffic surveillance and accident detection system (a smart way to handle traffic and concerned problems). In: Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–7, April 2017. https://doi.org/10.1109/IPACT.2017.8245066
Patil, D., Rosekind, M.: Traffic fatalities data has just been released: a call to action to download and analyze. US Department of Transportation (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. arXiv preprint (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
World Health Organization, et al.: The top 10 causes of death (2016). https://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death. May 2018
Ye, F., Qian, Y., Hu, R.Q.: Smart service-aware wireless mixed-area networks. IEEE Netw. 33(1), 84–91 (2019). https://doi.org/10.1109/MNET.2018.1700399
Yi, Z.: Evaluation and implementation of convolutional neural networks in image recognition. In: Journal of Physics: Conference Series, vol. 1087, p. 062018. IOP Publishing (2018)
Zhang, J., Ye, F., Qian, Y.: A distributed network QoE measurement framework for smart networks in smart cities. In: IEEE International Smart Cities Conference (ISC2), pp. 1–7, September 2018. https://doi.org/10.1109/ISC2.2018.8656854
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Yang, J., Zhang, J., Ye, F., Cheng, X. (2019). A UAV Based Multi-object Detection Scheme to Enhance Road Condition Monitoring and Control for Future Smart Transportation. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_23
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