Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
| Autor: | Andéol, Léo, Fel, Thomas, Grancey, Florence De, Mossina, Luca |
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| Přispěvatelé: | SNCF : Innovation & Recherche, SNCF, Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT), Brown University, Thales AVS France SAS, IRT Saint Exupéry - Institut de Recherche Technologique, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019) |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
conformal prediction
FOS: Computer and information sciences Computer Science - Machine Learning [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Statistics - Machine Learning conformal risk control Conformal Prediction Object Detection Uncertainty Quantification Object Detection railway signals Uncertainty Quantification Machine Learning (stat.ML) Machine Learning (cs.LG) |
| Popis: | Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential of the conformal prediction framework to evaluate model performance and provide practical guidance for achieving formally guaranteed uncertainty bounds. |
| Databáze: | OpenAIRE |
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