Conformal Prediction for Trustworthy Detection of Railway Signals

Léo Andéol; Thomas Fel; Florence De Grancey; Luca Mossina

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arXiv · 2301.11136

Conformal Prediction for Trustworthy Detection of Railway Signals

Léo Andéol·Thomas Fel·Florence De Grancey·Luca Mossina

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Abstract

We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tested and the most promising one undergoes the process of conformalization, where a correction is applied to the predicted bounding boxes (i.e. to their height and width) such that they comply with a predefined probability of success. We work with a novel exploratory dataset of images taken from the perspective of a train operator, as a first step to build and validate future trustworthy machine learning models for the detection of railway signals.

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Léo Andéol, Thomas Fel, Florence De Grancey, Luca Mossina. 2023-01-26. Conformal Prediction for Trustworthy Detection of Railway Signals. https://arxiv.org/abs/2301.11136

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