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.
Brandon Ramos López
Universidad de Guadalajara
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Javier Enrique Gómez Avila
Universidad de Guadalajara
0000-0002-9724-1729
Michel López Franco
Universidad de Guadalajara
0000-0002-4245-9683
Carlos López Franco
Universidad de Guadalajara
0000-0001-8122-3799
Acerca de
This paper presents a hybrid approach for detecting and segmenting wood defects, designed for deployment on low-resource embed- ded devices. The approach combines the efficiency of YOLO (You Only Look Once) for defect detection with traditional image processing techniques for precise segmentation. The use of traditional methods helps reduce the computational burden, making the approach more suitable for resource-constrained environments. This methodology leverages the strengths of both approaches while mitigating their individual limitations.
Referencias
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Ph.D. thesis, Corporación Universitaria Autónoma de Occidente, Ingeniería Industrial (1985), https://red.uao.edu.co/server/api/core/bitstreams/58fb9880-cd0b-4d42-9626-c5a7d52bd441/content
Ninja, D.: Wood defect detection dataset (2024), https://datasetninja.com/wood- defect-detection
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979). https://doi.org/10.1109/TSMC.1979.4310076
Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. arXiv preprint arXiv:1903.08536v3 (2019), https://arxiv.org/pdf/1903.08536v3
Ultralytics: Models-ultralytics documentation (2024), https://docs.ultralytics.com/models/, accessed: 2025-02-21
Unknown: Wood defect detection dataset (2021). https://doi.org/10.5281/zenodo.4694695, https://zenodo.org/records/4694695, download Dataset
Varghese, R., M., S.: Yolov8: A novel object detection algorithm with enhanced performance and robustness. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). pp. 1–6 (2024). https://doi.org/10.1109/ADICS58448.2024.10533619