Uso de YOLO v8 y aumento de datos para la detección de microplásticos en agua
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ene 29, 2025
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https://doi.org/10.36677/rici.v3i1.25148
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Octavio Villegas-Camacho
http://orcid.org/0009-0009-8017-0774
Iván Francisco-Valencia, Dr.
http://orcid.org/0000-0002-8736-4338
Roberto Alejo-Eleuterio, Dr.
http://orcid.org/0000-0002-7580-3305
Federico Del Razo-López
http://orcid.org/0000-0002-5241-5457
http://orcid.org/0009-0009-8017-0774
Iván Francisco-Valencia, Dr.
http://orcid.org/0000-0002-8736-4338
Roberto Alejo-Eleuterio, Dr.
http://orcid.org/0000-0002-7580-3305
Federico Del Razo-López
http://orcid.org/0000-0002-5241-5457
Resumen
Los microplásticos presentes en el agua se han convertido en un problema ambiental grave, por lo que ha surgido la necesidad de desarrollar técnicas y herramientas para su detección y posterior eliminación. En este estudio, se utilizó el modelo de visión por computadora YOLO (You Only Look Once) en su versión 8, para la detección de microplásticos en agua, el cual fue entrenado utilizando un conjunto de 500 imágenes obtenidas mediante microscopio estereoscópico, derivadas de muestras recolectadas en dos zonas del Estado de México: Valle de Bravo y el municipio de Malinalco. Adicionalmente a este conjunto de imágenes se le aplicaron diferentes técnicas de aumento de datos, incluyendo, Resize, Crop, Flip, Brightness and Contrast, HSV, Gamma Correction, CLAHE, RGB Shift y Gaussian Blur, con el objetivo de generar un conjunto más diverso. Los resultados obtenidos indican que YOLO, entrenado con el conjunto aumentado, alcanzó una precisión de 0.825 y un mAP@50 de 0.73, valores superiores a los logrados con el conjunto original sin aumentos. En conclusión, YOLO, en combinado con técnicas de aumento de datos, muestra ser eficaz para la detección de microplásticos, sentando las bases para su aplicación práctica en el monitoreo de cuerpos de agua.
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Como citar
VILLEGAS-CAMACHO, Octavio et al.
Uso de YOLO v8 y aumento de datos para la detección de microplásticos en agua.
Ideas en Ciencias de la Ingeniería, [S.l.], v. 3, n. 1, p. 72-87, ene. 2025.
ISSN 2992-7447.
Disponible en: <https://ideasencienciasingenieria.uaemex.mx/article/view/25148>. Fecha de acceso: 26 ago. 2026
doi: https://doi.org/10.36677/rici.v3i1.25148.
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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[3] M. A. B. Sarker, M. H. Imtiaz, T. M. Holsen y A. B. M. Baki, "Real-Time Detection of Microplastics Using an AI Camera," Sensors, vol. 24, nº 13, 2024. DOI: 10.3390/s24134394
[4] M. A. B. Sarker, U. Butt, M. H. Imtiaz y A. B. Baki, "Automatic Detection of Microplastics in the Aqueous Environment," 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0768-0772, 2023. DOI: 10.1109/CCWC57344.2023.10099253
[5] A. H. Iri, M. H. A. Shahrah, A. M. Ali, S. A. Qadri, T. Erdem, I. T. Ozdur y K. Icoz, "Optical detection of microplastics in water," Environmental Science and Pollution Research, 28, 63860–63866 (2021). DOI: 10.1007/s11356-021-12358-2
[6] Q. Zhang, E. G. Xu, J. Li, Q. Chen, L. Ma, E. Y. Zeng y H. Shi, "A review of microplastics in table salt, drinking water, and air: direct human exposure," Environmental science & technology, vol. 54, nº 7, pp. 3740--3751, 2020. DOI: 10.1021/acs.est.9b04535
[7] Z. Yan, Y. Liu, T. Zhang, F. Zhang, H. Ren y Y. Zhang, "Analysis of Microplastics in Human Feces Reveals a Correlation between Fecal Microplastics and Inflammatory Bowel Disease Status," Environmental Science & Technology, vol. 56, nº 1, pp. 414-421, 2022. DOI: 10.1021/acs.est.1c03924
[8] W. Gao, Y. Zhang, A. Mo, J. Jiang, Y. Liang, X. Cao y D. He, "Removal of microplastics in water: Technology progress and green strategies," Green Analytical Chemistry, vol. 3, p. 100042, 2022. DOI: 10.1016/j.greeac.2022.100042
[9] E. K. Dal y R. Kilic, "Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm," DOI: 10.21203/rs.3.rs-3890356/v1
[10] Y. Kryvenchuk y A. Marusyk, "Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks," 04 2024. URL: https://ceur-ws.org/Vol-3668/paper13.pdf
[11] H. Huang, H. Cai, J. U. Qureshi, S. R. Mehdi, H. Song, C. Liu, Y. Di, H. Shi, W. Yao y Z. Sun, "Proceeding the categorization of microplastics through deep learning-based image segmentation," Science of The Total Environment, vol. 2023, p. 165308, 896. DOI: 10.1016/j.scitotenv.2023.165308
[12] J. Xu y Z. Wang, "Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks," Science of The Total Environment, vol. 911, p. 168696, 2024. DOI: 10.1016/j.scitotenv.2023.168696
[13] M. Padervand, E. Lichtfouse, D. Robert y C. Wang, "Removal of microplastics from the environment. A review," Environmental Chemistry Letters, vol. 18, nº 3, pp. 807--828, 2020. DOI: 10.1007/s10311-020-00983-1
[14] T. Kumar, R. Brennan, A. Mileo y M. Bendechache, "Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions," IEEE Access, vol. 4, 2024. DOI: 10.48550/arXiv.2301.02830
[15] A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin y A. A. Kalinin, "Albumentations: Fast and Flexible Image Augmentations," Information, vol. 11, nº 2, 2020. DOI: 10.3390/info11020125
[16] M. Hussain, "YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection," Machines, vol. 11, nº 7, p. 677, 2023. DOI: 10.3390/machines11070677
[17] J. Terven, D.-M. Córdova-Esparza y J.-A. Romero-González, "A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS," Machine Learning and Knowledge Extraction, vol. 5, nº 4, pp. 1680--1716, 2023. DOI: 10.3390/make5040083
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