Detección de eventos delictivos usando deep learning: un caso de estudio en el TEST
Article Sidebar
Publicado
ene 29, 2025
##plugins.pubIds.doi.readerDisplayName##
https://doi.org/10.36677/rici.v3i1.25167
Main Article Content
Luisa Fernanda Ballesteros-Reyes
http://orcid.org/0009-0007-5394-8654
Laura Cleofas-Sánchez
http://orcid.org/0000-0002-6666-0461
Angélica Guzmán-Ponce
http://orcid.org/0000-0002-7844-1266
Hugo Yañez-Badillo
http://orcid.org/0000-0002-2433-1475
Anabel Pineda-Briseño
http://orcid.org/0000-0002-5296-4210
http://orcid.org/0009-0007-5394-8654
Laura Cleofas-Sánchez
http://orcid.org/0000-0002-6666-0461
Angélica Guzmán-Ponce
http://orcid.org/0000-0002-7844-1266
Hugo Yañez-Badillo
http://orcid.org/0000-0002-2433-1475
Anabel Pineda-Briseño
http://orcid.org/0000-0002-5296-4210
Resumen
En 2023, se observó un incremento en la incidencia y prevalencia de actos delictivos en México, con variaciones significativas entre las diferentes entidades federativas. El Estado de México destacó particularmente por registrar un aumento en los niveles de eventos delictivos con respecto al año anterior. Los delitos más comunes incluyeron robos perpetrados mediante distintas modalidades, destacando el uso de armas blancas y de fuego, así como agresiones físicas directas. Por otro lado, la violencia en los entornos escolares en México ha mostrado tendencias alarmantes en varios tipos de agresión. De lo antes mencionado, se destaca la importancia de implementar técnicas avanzadas que permitan la identificación oportuna de hechos delictivos en entornos escolares a fin de salvaguardar la integridad de los estudiantes. Este estudio explora el uso del modelo YOLOv5s para la detección de arrebatos, armas blancas y de fuego, específicamente en el Tecnológico de Estudios Superiores de Tianguistenco utilizado imágenes digitales obtenidas a través de cámaras fotográficas. Los resultados obtenidos indican que este modelo logra clasificar incidentes que involucran el uso de armas blancas, armas de fuego y arrebatos, alcanzando un promedio de precisión media del 60 %.
Article Details
Como citar
BALLESTEROS-REYES, Luisa Fernanda et al.
Detección de eventos delictivos usando deep learning: un caso de estudio en el TEST.
Ideas en Ciencias de la Ingeniería, [S.l.], v. 3, n. 1, p. 88-100, ene. 2025.
ISSN 2992-7447.
Disponible en: <https://ideasencienciasingenieria.uaemex.mx/article/view/25167>. Fecha de acceso: 27 ago. 2026
doi: https://doi.org/10.36677/rici.v3i1.25167.
Sección
Artículos
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
[1] A. Lankford. “The importance of analyzing public mass shooters separately from other attackers when estimating the prevalence of their behavior worldwide”. Econ journal, vol. 17, no. 1, pp. 40-55, 2020. https://journaltalk.net/articles/5999/
[2] D. Arboleda Andrade, "Tiroteo en Stoneman Douglas: discursos políticos, funcionamiento y materialidades discursivas del suceso," Revista Científica de comunicación, vol. 10, no. 1, pp. 49-68, 2019. DOI: https://doi.org/10.31207/rch.v10i1.177
[3] D. Perez Esparza, S.D. Johnson, P. Gill. “Why did Mexico become a violent country?” Assessing the role of firearms trafficked from the US. Security journal, vol. 33, pp. 179–209, 2020. DOI:10.1057/s41284-019-00178-6
[4] E. Weigend Vargas, M. Degli Esposti, S. Hargarten.”Examining firearm-related deaths in Mexico”. vol. 11, no. 33, pp. 2–7, 2024. DOI:10.1186/s40621-024-00519-z
[5] Instituto Nacional de Estadística y Geografía. INEGI; 2023. https://en. www.inegi.org.mx/inegi/acercade.html. Consultado el 1 Noviembre 2024.
[6] A. Pulido Gómez., & B. J. Almaraz Calderón., (2017). “Violencia y comportamiento electoral: el caso del Estado de México”. Apuntes Electorales, vol. 16, no. 56, pp. 9–38. Recuperado a partir de https://aelectorales.ieem.org.mx/index.php/ae/article/view/80
[7] J. E Goldstick., R. M. Cunningham, P. M. Carter. “Current Causes of Death in Children and Adolescents in the United States”. New England Journal of Medicine, vol. 386, no. 20, pp. 1955-1956, 2022. DOI: 10.1056/nejmc2201761
[8] F. Dakalbab, M. A. Talib, O. A. Waraga, A. B. Nassif, S. Abbas, and Q. Nasir, “Artificial intelligence & crime prediction: A systematic literature review”. Social Sciences & Humanities Open, vol. 6, no. 1, pp. 1-23, 2022. DOI: 10.1016/j.ssaho.2022.100342
[9] A. M. Al-Madani, V. Mahale, A. T. Gaikwad, “Real-time detection of crime and violence in video surveillance using deep learning”. First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022). Atlantis Press, 2023, pp. 431–441. DOI: 10.2991/978-94-6463-196-8_33
[10] P. Sivakumar, V. Jayabalaguru, R. Ramsugumar, S. Kalaisriram. “Real time crime detection using deep learning algorithm”. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2021, pp. 1–5. DOI: 10.1109/ICSCAN53069.2021.9526393
[11] M. M. Mukto, M. Hasan, M. M. Al Mahmud, I. Haque, M. A. Ahmed, T. Jabid, M. S. Ali, M. R. A. Rashid, M. M. Islam, M. Islam, “Design of a real-time crime monitoring system using deep learning techniques”. Intelligent Systems with Applications, vol. 21, pp.1-17, 2024. DOI: 10.1016/j.iswa.2023.200311
[12] A. O. Hashi, A. A. Abdirahman, M. A. Elmi, O. E. R. Rodriguez, “Deep learning models for crime intention detection using object detection”. International Journal of Advanced Computer Science and Applications, vol. 14, no. 4, pp. 300-306, 2023. DOI: 10.14569/IJACSA.2023.0140434
[13] S. Sandhya, A. Balasundaram, A. Shaik, “Deep learning based face detection and identification of criminal suspects”. Computers, Materials & Continua, vol. 74, no. 2, pp. 2331-2343, 2023. DOI: doi.org/10.32604/cmc.2023.032715.
[14] K. Jenga, C. Catal, G. Kar, “Machine learning in crime prediction”. Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3, pp. 2887–2913, 2023. DOI: 10.1007/s12652-023-04530-y
[15] J. R. R. Kumar, D. Dhabliya, S. S. Dari, “A comparative study of machine learning algorithms for image recognition in privacy protection and crime detection”. International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 482–490, 2023. ISSN:2147-679921
[16] P. Stalidis, T. Semertzidis, P. Daras, “Examining deep learning architectures for crime classification and prediction”. Forecasting, vol. 3, no. 4, pp. 741–762, 2021. DOI: 10.3390/forecast3040046
[17] M. Divya, G. S. Priya, R. Abitha, K. Sirisha, A. Manikanta, K. Jayanth, “Automated crime intention detection using deep learning”. Int. Resea. J. of Modernization in Engg. Tech., and Sci, vol. 4, no. 6, pp. 5670–5676, 2022. e-ISSN: 2582-5208
[18] R. J. Anandhi, "Edge Computing-Based Crime Scene Object Detection from Surveillance Video Using Deep Learning Algorithms," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 1159-1163. DOI: 10.1109/ICIRCA57980.2023.10220800.
[19] M. Alruwaili et al. " Deep Learning-Based YOLO Models for the Detection of People With Disabilities". In IEEE Access, vol. 12, pp. 2543-2566, 2024. DOI: 10.1109/ACCESS.2023.3347169.
[20] K. Jun-Hwa, K. Namho, P. Yong Woon, W. Chee Sun. “Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset”. Journal of Marine Science and Engineering. vol. 10, no. 3, pp. 2–14, 2022. DOI: 10.3390/jmse10030377
[2] D. Arboleda Andrade, "Tiroteo en Stoneman Douglas: discursos políticos, funcionamiento y materialidades discursivas del suceso," Revista Científica de comunicación, vol. 10, no. 1, pp. 49-68, 2019. DOI: https://doi.org/10.31207/rch.v10i1.177
[3] D. Perez Esparza, S.D. Johnson, P. Gill. “Why did Mexico become a violent country?” Assessing the role of firearms trafficked from the US. Security journal, vol. 33, pp. 179–209, 2020. DOI:10.1057/s41284-019-00178-6
[4] E. Weigend Vargas, M. Degli Esposti, S. Hargarten.”Examining firearm-related deaths in Mexico”. vol. 11, no. 33, pp. 2–7, 2024. DOI:10.1186/s40621-024-00519-z
[5] Instituto Nacional de Estadística y Geografía. INEGI; 2023. https://en. www.inegi.org.mx/inegi/acercade.html. Consultado el 1 Noviembre 2024.
[6] A. Pulido Gómez., & B. J. Almaraz Calderón., (2017). “Violencia y comportamiento electoral: el caso del Estado de México”. Apuntes Electorales, vol. 16, no. 56, pp. 9–38. Recuperado a partir de https://aelectorales.ieem.org.mx/index.php/ae/article/view/80
[7] J. E Goldstick., R. M. Cunningham, P. M. Carter. “Current Causes of Death in Children and Adolescents in the United States”. New England Journal of Medicine, vol. 386, no. 20, pp. 1955-1956, 2022. DOI: 10.1056/nejmc2201761
[8] F. Dakalbab, M. A. Talib, O. A. Waraga, A. B. Nassif, S. Abbas, and Q. Nasir, “Artificial intelligence & crime prediction: A systematic literature review”. Social Sciences & Humanities Open, vol. 6, no. 1, pp. 1-23, 2022. DOI: 10.1016/j.ssaho.2022.100342
[9] A. M. Al-Madani, V. Mahale, A. T. Gaikwad, “Real-time detection of crime and violence in video surveillance using deep learning”. First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022). Atlantis Press, 2023, pp. 431–441. DOI: 10.2991/978-94-6463-196-8_33
[10] P. Sivakumar, V. Jayabalaguru, R. Ramsugumar, S. Kalaisriram. “Real time crime detection using deep learning algorithm”. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2021, pp. 1–5. DOI: 10.1109/ICSCAN53069.2021.9526393
[11] M. M. Mukto, M. Hasan, M. M. Al Mahmud, I. Haque, M. A. Ahmed, T. Jabid, M. S. Ali, M. R. A. Rashid, M. M. Islam, M. Islam, “Design of a real-time crime monitoring system using deep learning techniques”. Intelligent Systems with Applications, vol. 21, pp.1-17, 2024. DOI: 10.1016/j.iswa.2023.200311
[12] A. O. Hashi, A. A. Abdirahman, M. A. Elmi, O. E. R. Rodriguez, “Deep learning models for crime intention detection using object detection”. International Journal of Advanced Computer Science and Applications, vol. 14, no. 4, pp. 300-306, 2023. DOI: 10.14569/IJACSA.2023.0140434
[13] S. Sandhya, A. Balasundaram, A. Shaik, “Deep learning based face detection and identification of criminal suspects”. Computers, Materials & Continua, vol. 74, no. 2, pp. 2331-2343, 2023. DOI: doi.org/10.32604/cmc.2023.032715.
[14] K. Jenga, C. Catal, G. Kar, “Machine learning in crime prediction”. Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3, pp. 2887–2913, 2023. DOI: 10.1007/s12652-023-04530-y
[15] J. R. R. Kumar, D. Dhabliya, S. S. Dari, “A comparative study of machine learning algorithms for image recognition in privacy protection and crime detection”. International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 482–490, 2023. ISSN:2147-679921
[16] P. Stalidis, T. Semertzidis, P. Daras, “Examining deep learning architectures for crime classification and prediction”. Forecasting, vol. 3, no. 4, pp. 741–762, 2021. DOI: 10.3390/forecast3040046
[17] M. Divya, G. S. Priya, R. Abitha, K. Sirisha, A. Manikanta, K. Jayanth, “Automated crime intention detection using deep learning”. Int. Resea. J. of Modernization in Engg. Tech., and Sci, vol. 4, no. 6, pp. 5670–5676, 2022. e-ISSN: 2582-5208
[18] R. J. Anandhi, "Edge Computing-Based Crime Scene Object Detection from Surveillance Video Using Deep Learning Algorithms," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 1159-1163. DOI: 10.1109/ICIRCA57980.2023.10220800.
[19] M. Alruwaili et al. " Deep Learning-Based YOLO Models for the Detection of People With Disabilities". In IEEE Access, vol. 12, pp. 2543-2566, 2024. DOI: 10.1109/ACCESS.2023.3347169.
[20] K. Jun-Hwa, K. Namho, P. Yong Woon, W. Chee Sun. “Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset”. Journal of Marine Science and Engineering. vol. 10, no. 3, pp. 2–14, 2022. DOI: 10.3390/jmse10030377