Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform
Railcar components inspection is one of the most critical tasks in railway maintenance. The use of image processing, coupled with machine learning, has emerged as a solution for replacing current standard methodologies. The spectral analysis gives the frequency representation of a signal and has been largely used in signal processing tasks. In this sense, this work proposes the evaluation of the use of the Discrete Fourier Transform (DFT) in addition to the spatial representation image of railcar components for an automatic detector of defective parts performed by Convolutional Neural Network (CNN) classi cation. The results are given in measures of accuracy, precision, recall, and F1-score metrics in addition to the accuracy boxplot, and showed that the use of the DFT increase in 1.04% the CNN classi cation accuracy.
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