Conformal Prediction for Trustworthy Detection of Railway Signals

• URL: http://arxiv.org/abs/2301.11136v1
• Date: Thu, 26 Jan 2023 14:40:49 GMT
• Title: Conformal Prediction for Trustworthy Detection of Railway Signals
• Authors: L\'eo And\'eol (IMT), Thomas Fel, Florence De Grancey, Luca Mossina
• Abstract summary: We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. We work with a novel exploratory dataset of images taken from the perspective of a train operator.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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