Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways
Resumo
In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.
Palavras-chave:
convolutional neural networks, YOLO algorithm, vehicles in motion
Referências
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Blosseville, J., Krafft, C., Lenoir, F., Motyka, V., and Beucher, S. (1989). Titan: A traf- fic measurement system using image processing techniques. In Second International Conference on Road Traffic Monitoring, 1989., pages 84–88. IET.
Buch, N., Velastin, S. A., and Orwell, J. (2011). A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Sys- tems, 12(3):920–939.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning. vol. 1.
Junior, C. and da Silva, V. (2019). Fatores associados aos acidentes de trânsito graves envolvendo condutores de automóvel e motocicleta: uma análise para as br 101, 116 e 230 na região nordeste em 2007 e 2016. Master’s thesis, UFRN.
Koubaa, A. and Qureshi, B. (2018). Dronetrack: Cloud-based real-time ob- ject tracking using unmanned aerial vehicles over the internet. IEEE Ac- cess:10.1109/ACCESS.2018.2811762.
Lee, S. E., Llaneras, E., Klauer, S., and Sudweeks, J. (2007). Analyses of rear-end crashes and near-crashes in the 100-car naturalistic driving study to support rear-signaling countermeasure development. DOT HS, 810:846.
Lu, M.-C., Hsu, C.-C., Lu, Y. Y., et al. (2007). Improvements and application of the image-based distance measuring system. In Proc. WSEAS Int. Conf.(CISST), pages 17–19.
M. H. Putra, Z. M. Yussof, K. C. L. S. I. S. Convolutional neu- ral network for person and car detection using yolo framework. JTEC:http://journal.utem.edu.my/index.php/jtec/article/view/3599.
Mandhare, P., Kharat, V., and Patil, C. (2018). Intelligent road traffic control system for traffic congestion a perspective. International Journal of Computer Sciences and Engineering, 6:908–915.
Menezes, R. S. T. d., Magalhaes, R. M., and Maia, H. (2019). Object recognition using convolutional neural networks. In Artificial Neural Networks. IntechOpen.
Organization, W. H. (2008). Global status report on road safety.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Wada, K. (2016). labelme: Image Polygonal Annotation with Python. https: //github.com/wkentaro/labelme.
Zear, A., Singh, P., and Singh, Y. (2016). Intelligent transport system: A progressive review. Indian Journal of Science and Technology, 9.
Publicado
30/06/2020
Como Citar
BASTOS, Mateus Eloi da Silva; FREITAS, Vitor Yeso Fidelis; DE MENEZES, Richardson Santiago Teles; MAIA, Helton .
Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 47. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 258-268.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2020.11334.
