The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the low-light environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly-released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system

Anomaly Detection, Localization and Classification for Railway Inspection / Riccardo Gasparini; Andrea D'Eusanio; Guido Borghi; Stefano Pini; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiara. - ELETTRONICO. - (2021), pp. 9412972.3419-9412972.3426. (Intervento presentato al convegno 25th International Conference of Pattern Recognition tenutosi a Milan nel 10-15 January 2021) [10.1109/ICPR48806.2021.9412972].

Anomaly Detection, Localization and Classification for Railway Inspection

Guido Borghi;
2021

Abstract

The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the low-light environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly-released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system
2021
2020 25th International Conference on Pattern Recognition (ICPR)
3419
3426
Anomaly Detection, Localization and Classification for Railway Inspection / Riccardo Gasparini; Andrea D'Eusanio; Guido Borghi; Stefano Pini; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiara. - ELETTRONICO. - (2021), pp. 9412972.3419-9412972.3426. (Intervento presentato al convegno 25th International Conference of Pattern Recognition tenutosi a Milan nel 10-15 January 2021) [10.1109/ICPR48806.2021.9412972].
Riccardo Gasparini; Andrea D'Eusanio; Guido Borghi; Stefano Pini; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiara
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/859633
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