Detection of traffic accidents using artificial intelligence
Abstract
This article analyzes different architectures with which a neural network can be developed using computer vision with the objective of detecting traffic accidents. For the development of the software, the Java Script programming language was used, reaching the conclusion that the best architecture to use is a Convolutional Neural Network since it has the capabilities of detecting features within the images. At the same time, a database was developed with the necessary characteristics for the functioning of the neural network.
Downloads
References
[2] H. S. Sánchez Restrepo, L. Chias Becerril, H. Reséndiz López, "Evolución de los accidentes de tránsito en las zonas urbanas y suburbanas de México en el periodo 1997-2016: mayor exposición al riesgo y menor letalidad". Revista Gerencia y Políticas de Salud, vol.18, n.37, Julio-Diciembre 2019, pp. 1–16. https://doi.org/10.11144/Javeriana.rgps18-37.eatz
[3] C. F. Alastruey, "The Impact of Advances in Artificial Intelligence, Autonomous Learning Systems, and Science". Revista Sociología y Tecnociencia, vol. 11, n. 2, published: 12/11/2021, pp. 182–195. ISSN 19898487. DOI: https://doi.org/10.24197/st.Extra_2.2021
[4] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, and H. Adam. "Mobilenets: Efficient convolutional neural networks for mobile vision applications". Revista arXiv preprint arXiv:1704.04861, publicado abr. 2017, Disponible en: http://arxiv.org/abs/1704.04861
[5] S. Kharuf-Gutierrez, L. Hernández Santana, R. Orozco Morales, O. de la C. Aday Díaz, and I. Delgado Mora, "Análisis de imágenes multiespectrales adquiridas con vehículos aéreos no tripulados", Revista EAC, La Habana, v. 39, n. 2, p. 79-91, agosto 2018. Disponible en: http://scielo.sld.cu/
[6] A. J. Díaz Ortíz, J. M. Martínez Zaragoza, J. N. García Matías, " Propuesta de Entrenamiento de Red Neuronal Artificial Para la Prevención de Accidentes Carretero " European Scientific Journal July 2019 edition Vol.15, No.21 ISSN: 18577881 (Print) e - ISSN 18577431. http://dx.doi.org/10.19044/esj.2019.v15n21p18
[7] A. S. Kristensen, D. Ahsan, S. Mehmood, y S. Ahmed, "Rescue Emergency Drone for Fast Response to Medical Emergencies Due to Traffic Accidents". World Academy of Science, Engineering and Technology, Open Science Index 131, International Journal of Health and Medical Engineering, vol. 11, n. 11, p. 637 - 641, publicado 2017. http://dx.doi.org/10.19044/esj.2019.v15n21p18
[8] J. E. Márquez Díaz "Seguridad metropolitana mediante el uso coordinado de Drones", Rev. Ing. USBMed, vol. 9, n.1, pp. 39-48, publicado en 2018, ISSN-e 2027-5846.
[9] C. Pérez Bernal, "Creación de un simulador y una IA sobre drones para la ayuda al rescate de montaña", sep. 2018, Accedido: abr. 23, 2020. https://riunet.upv.es/handle/10251/107279
[10] G. L. Hart, T. Mueller, C. Toher, "Machine learning for alloys", p. 730–755. https://doi.org/10.1038/s41578-021-00340-w, Published 20 July 2021.
[11] A. Martín, A Ashish, B. Paul, B. Eugene, C. Zhifeng, C. Craig, Z. Xiaoqiang. " TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems". Revista arXiv preprint arXiv: 1603.04467, publicado marzo. 2016, Accedido: junio 18, 2021. Disponible en: https://arxiv.org/abs/1603.04467. https://doi.org/10.48550/arXiv.1603.04467
[12] K. P. Carpio Peláez, F. Oñate Valdivieso. "Redes neuronales artificiales aplicadas en sistemas de predicción para la seguridad vial", v. 17, n. 2. pagina 1-16. publicado en el 2020. https://doi.org/10.18041/1794-4953/avances.2.6632
[13] E. Bodero, M. P. López, A. E. Congacha, E. Cajamarca and C. H. Morales. “Google Colaboratory como alternativa para el procesamiento de una red neuronal convolucional”. Vol. 41, Nº 7. Pág. 1-22. ISSN 0798 1015. Publicado en la revista espacios 05/03/2020.
[14] W.Q.Yan "Deep Learning Platforms. In: Computational Methods for Deep Learning. Texts in Computer Science". Publisher in Springer 2021, Cham. https://doi.org/10.1007/978-3-030-61081-4_2, Print ISBN 978-3-030-61080-7 Online ISBN 978-3-030-61081-4.
[15] C. V. Niño Rondón, D. A. Castellano Carvajal, S. A. Castro Casadiego, B. Medina Delgado, and D. Guevara Ibarra. "Detección de placas vehiculares mediante modelo de clasificador en cascada basado en lenguaje Python", Publisher in Eco Matemático Scientific Journal of mathematics, enero 2021. v.12, n.1. ISSN:17948231, https://doi.org/10.22463/17948231.3068
Copyright (c) 2024 ITEGAM-JETIA
This work is licensed under a Creative Commons Attribution 4.0 International License.