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.
@article{arxiv.2301.11136,
title = {Conformal Prediction for Trustworthy Detection of Railway Signals},
author = {Léo Andéol and Thomas Fel and Florence De Grancey and Luca Mossina},
journal= {arXiv preprint arXiv:2301.11136},
year = {2023}
}