Abstract:
Improving the level of safety of railway traffic is directly related to the need for prompt detection of structural anomalies of track elements. This task is implemented through regular inspections using non-destructive testing methods. Among the modern technologies used for this purpose, eddy current flaw detection stands out. The flaw detector generates a multi-channel discrete signal, which is called a defectogram. Defectograms require analysis, that is, the identification of useful signals from a defect or structural elements of the rail. This paper investigates the use of YOLO (You Only Look Once) family convolutional neural networks for automated detection of useful signals in eddy current rail defectograms. The main objective was to evaluate the effectiveness of different transformations of multichannel time-series data into two-dimensional images suitable for YOLO processing, and to explore the trade-off between detection accuracy and computational cost. Four transformation methods are examined: Threshold Transform, based on amplitude comparisons against a twice threshold noise level, Short-Time Fourier Transform, Continuous Wavelet Transform and Hilbert–Huang Transform. The dataset comprises defectogram fragments of 50 thousand counts with annotated useful signals from three classes (flash butt welds, aluminothermic welds, and bolt joints), split into training, validation, and test sets. YOLO models trained on this data achieved high mean Average Precision scores in useful signals detection for all considered transformation methods. Continuous Wavelet Transform yielded the best scores while the Threshold Transform proved to be the least computationally expensive. Short-Time Fourier Transform method offered the best balance between precision and recall. Hilbert–Huang Transform showed slightly lower effectiveness. These results demonstrate the suitability of YOLO networks for eddy current defectogram analysis and useful signals detection in general.
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