Publications
2015
1.
García-Olalla, Óscar; Alegre, Enrique; Barreiro, Joaquín; Fernández-Robles, Laura; García-Ordás, María Teresa
Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers Artículo de revista
En: Procedia engineering, vol. 132, pp. 950–957, 2015, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: LBP, LOSIB, Monitoring, TCM, texture description, Tool wear
@article{garcia-olalla_tool_2015,
title = {Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers},
author = {Óscar García-Olalla and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},
url = {https://www.sciencedirect.com/science/article/pii/S187770581504494X},
year = {2015},
date = {2015-01-01},
journal = {Procedia engineering},
volume = {132},
pages = {950–957},
abstract = {This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).},
note = {Publisher: No longer published by Elsevier},
keywords = {LBP, LOSIB, Monitoring, TCM, texture description, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).