Abstract
Vehicular networks are gaining attention for their applicability in safe driving support systems. There are a variety of route planning and safety applications using vehicular networks such as emergency disaster warning, intersection conflict warning and traffic congestion warning applications. Sharing of information between vehicles on fallen objects on the road is very important for safe driving. In this paper, we propose an intelligent fallen object detection system for improving the safe driving. We focus on boxes, cans, pet bottles and plastics that often fall on the road. From the evaluation results, we observed that our system has a good performance for box, can and plastic objects.
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References
Kaggle: Data science community https://www.kaggle.com/
Bergasa, L.M., Almeria, D., Almazan, J., Yebes, J.J., Arroyo, R.: DriveSafe: an app for alerting inattentive drivers and scoring driving behaviors. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2014, pp. 240–245 (2014)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: Optimal speed and accuracy of object detection. Computer Vision and Pattern Recognition (cs.CV) (April 2020). https://arxiv.org/abs/2004.10934
Ersal, T., Fuller, H.J.A., Tsimhoni, O., Stein, J.L., Fathy, H.K.: Model-based analysis and classification of driver distraction under secondary tasks. IEEE Trans. Intell. Transp. Syst. 11(3), 692–701 (2010)
Proença, F.P., Simões, P.: TACO: Trash annotations in context for litter detection, Computer Vision and Pattern Recognition (cs.CV) (March 2020). http://tacodataset.org/
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Le, Q.V.: Building high-level features using large scale unsupervised learning. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP-2013), pp. 8595–8598 (May 2013)
Liu, T., Yang, Y., Huang, G.B., Yeo, Y.K., Lin, Z.: Driver distraction detection using semi-supervised machine learning. IEEE Trans. Intell. Transp. Syst. 17(4), 1108–1120 (2016)
Long, X., et al.: PP-YOLO: An effective and efficient implementation of object detector, Computer Vision and Pattern Recognition (cs.CV) (July 2020). https://arxiv.org/pdf/2007.12099v3.pdf
Majchrowska, S., et al.: Waste detection in pomerania: non-profit project for detecting waste in environment. Computer Vision and Pattern Recognition (cs.CV) (2021). https://arxiv.org/abs/2105.06808
McCall, J.C., Trivedi, M.M.: Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95(2), 374–387 (2007)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (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 (CVPR-2016), pp. 779–788 (June 2016)
Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434–3445 (2016)
Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
Silver, D., et al.: Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR-2015) (May 2015)
Vicente, F., Huang, Z., Xiong, X., la Torre, F.D., Zhang, W., Levi, D.: Driver gaze tracking and eyes offthe road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)
Wang, Y.K., Jung, T.P., Lin, C.T.: EEG-based attention tracking during distracted driving. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1085–1094 (2015)
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Uchimura, S., Tada, Y., Ikeda, M., Barolli, L. (2022). An Intelligent Fallen Object Detection System for Safe Driving. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2021. Lecture Notes in Networks and Systems, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-90072-4_34
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