A vision-based approach for tramway rail extraction

Zwemer, Matthijs H.; Wouw, Dennis W. J. M. van de; Jaspers, E.G.T.; Zinger, Sveta

doi:10.1117/12.2075641

Cited by 6 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anomaly Detection on Railways In general, a very limited amount of works exploit visual data in the railway scenario, for similar tasks such as anti-collision prediction [21], [22] and track detection [23], [24], [25]. Only few works tackle the task of anomaly detection applied on this specific context.…”

Section: Related Work Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection, Localization and Classification for Railway Inspection

Gasparini¹,

D’Eusanio²,

Borghi³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

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 lowlight 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 publiclyreleased 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.

show abstract

Section: Related Work Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection, Localization and Classification for Railway Inspection

Gasparini¹,

D’Eusanio²,

Borghi³

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

show abstract

“…At the time of writing, there are no works that address the task of anomaly detection through visual data in the railway scenario during nighttime. Only similar task have been addressed, such as track detection [12,17,36] and collision prediction [22,23]. Unfortunately, datasets are not often publicly available.…”

Section: Anomaly Detection On Railwaysmentioning

confidence: 99%

Anomaly Detection for Vision-Based Railway Inspection

Gasparini

Pini

Borghi

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

The automatic inspection of railways for the detection of obstacles is a fundamental activity in order to guarantee the safety of the train transport. Therefore, in this paper, we propose a vision-based framework that is able to detect obstacles during the night, when the train circulation is usually suspended, using RGB or thermal images. Acquisition cameras and external light sources are placed in the frontal part of a rail drone and a new dataset is collected. Experiments show the accuracy of the proposed approach and its suitability, in terms of computational load, to be implemented on a self-powered drone.

show abstract