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Deploying deep learning models in real-world certified systems requires theability to provide confidence estimates that accurately reflect theiruncertainty. In this paper, we demonstrate the use of the conformal predictionframework to construct reliable and trustworthy predictors for detectingrailway signals. Our approach is based on a novel dataset that includes imagestaken from the perspective of a train operator and state-of-the-art objectdetectors. We test several conformal approaches and introduce a new methodbased on conformal risk control. Our findings demonstrate the potential of theconformal prediction framework to evaluate model performance and providepractical guidance for achieving formally guaranteed uncertainty bounds.

Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling