ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Dulce María Ibarra Montes
Universidad de Guadalajara
0009-0002-2939-3066
Francisco González Barriga
Universidad de Guadalajara
0009-0005-0340-4419
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Gabriel Martínez Soltero
Universidad de Guadalajara
0000-0003-3538-8132
Acerca de
There are a lot of reasons why a road accident happens, including road conditions, weather, car failures and human errors among others, that aren’t easy to avoid or prevent, that’s why in the last decade the technology has gained a main role when it comes to avoid these acci- dents as the Advance Drivers Assistance System or ADAS, where some of these technologies try to minimize road accidents due to lack of attention of the drivers on the road by detecting whether the driver is drowsy or dis- tracted and notify him. However, not all vehicles are equipped with such systems. This paper presents a cost-effective and straightforward solution by implementing a quantized Convolutional Neural Network (CNN) on a low-cost module, achieving an accuracy of 86.7% of accuracy.
Referencias
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., Miao, Y.: Review of image classification algorithms based on convolutional neural networks. Remote Sensing 13(22), 4712 (2021)
Chitty-Venkata, K.T., Somani, A.K.: Neural architecture search survey: A hard- ware perspective. ACM Computing Surveys 55(4), 1–36 (2022)
David, R., Duke, J., Jain, A., Janapa Reddi, V., Jeffries, N., Li, J., Kreeger, N., Nappier, I., Natraj, M., Wang, T., et al.: Tensorflow lite micro: Embedded machine learning for tinyml systems. Proceedings of Machine Learning and Systems 3, 800– 811 (2021)
Dietz, H., Abney, D., Eberhart, P., Santini, N., Davis, W., Wilson, E., McKenzie, M.: Esp32-cam as a programmable camera research platform. Imaging 232(2), 10–2352 (2022)
Janstrup, K.H.: Road safety annual report 2017 (2017)
Jumaa, B.A., Abdulhassan, A.M., Abdulhassan, A.M.: Advanced driver assistance system (adas): A review of systems and technologies. International Journal of Ad- vanced Research in Computer Engineering & Technology (IJARCET) 8(6), 231–4 (2019)
Kadhim, T.A., Hariri, W., Smaoui Zghal, N., Ben Aissa, D.: A face recognition application for alzheimer’s patients using esp32-cam and raspberry pi. Journal of Real-Time Image Processing 20(5), 100 (2023)
Khan, M.Q., Lee, S.: Gaze and eye tracking: Techniques and applications in adas. Sensors 19(24), 5540 (2019)
Nemavhola, A., Chibaya, C., Viriri, S.: A systematic review of cnn architectures, databases, performance metrics, and applications in face recognition. Information 16(2), 107 (2025)
Saini, V., Saini, R.: Driver drowsiness detection system and techniques: a review. International Journal of Computer Science and Information Technologies 5(3), 4245–4249 (2014)