Título: Deep Rule-Based Approach for the Classification of Wagon Bogie Springs Condition
Autores: Carlos Manuel Viriato Neto, Luca Garcia and Eduardo Aguiar.
Resumo:
This paper focuses on the new model of classification of wagon bogie springs condition through images acquired by a wayside equipment. As such, we are discussing the application of a deep rule-based (DRB) classifier learning approach to achieve ahigh classification of a bogie, and check if they either have spring problems or not. We use a pre-trained VGG19 deep convolutional neural network to extract the attributes from images to be used as input to the classifiers. The performance is calculated based on the data set composed of images provided by a Brazilian railway company. The presented results of the report demonstrate the relative performance of applying the DRB classifier to the questions raised.
Palavras-chave:
DRB, Defects, pre-trained VGG19, Wagon bogie springs, Image processing.
Páginas: 6
Código DOI: 10.21528/CBIC2021-48
Artigo em pdf: CBIC_2021_paper_48.pdf
Arquivo BibTeX: CBIC_2021_48.bib