ANOMALY DETECTION IN RAILWAY IMAGES USING UNSUPERVISED CLUSTERING OF INFRARED THERMOGRAPHY


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Authors

  • Mehmet Fidan Eskisehir Technical University
  • Murat Başaran Eskisehir Technical University
  • Mine Sertsöz Eskisehir Technical University
  • Ömür Akbayır Eskisehir Technical University

DOI:

https://doi.org/10.5281/zenodo.12704340

Keywords:

Anomaly detection, Infrared thermography, Railway images, Unsupervised clustering

Abstract

This paper presents a novel system for the unsupervised detection of anomalies in railway images using infrared thermography. The system comprises three main stages: image preprocessing, feature extraction, and clustering. In the image preprocessing stage, the infrared images are enhanced and normalized to improve the subsequent feature extraction. In the feature extraction stage, a set of relevant features is extracted from the preprocessed images. Finally, in the clustering stage, unsupervised clustering algorithms are employed to group the extracted features into different clusters, each representing a specific anomaly type. The proposed system is evaluated on a real-world dataset of railway images captured by a thermal camera. The experimental results demonstrate the effectiveness of the proposed system in detecting various types of anomalies with high accuracy.

References

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Oliveira, D. F., Vismari, L. F., de Almeida, J. R., Cugnasca, P. S., Camargo, J. B., Marreto, E., ... & Neves, M. M. Evaluating unsupervised anomaly detection models to detect faults in heavy haul railway operations, in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). 2019. pp. 1016-1022. IEEE.

da Silva Ferreira, M., Vismari, L. F., Cugnasca, P. S., de Almeida, J. R., Camargo, J. B., & Kallemback, G. A comparative analysis of unsupervised learning techniques for anomaly detection in railway systems, in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). 2019. pp. 444-449. IEEE.

Ghiasi, R., Khan, M. A., Sorrentino, D., Diaine, C., & Malekjafarian. An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine. Engineering Applications of Artificial Intelligence, 2019. 133. 108167.

Published

2024-07-15

How to Cite

Fidan, M., Başaran, M., Sertsöz, M., & Akbayır, Ömür. (2024). ANOMALY DETECTION IN RAILWAY IMAGES USING UNSUPERVISED CLUSTERING OF INFRARED THERMOGRAPHY. Journal of Natural Sciences and Technologies, 3(1), 259–261. https://doi.org/10.5281/zenodo.12704340