A safety-critical control system and method with train-time calibration of object detection. A controller calibrates prediction by a deep neural network. The train-time calibration includes a multi-class confidence calibration, and a bounding box localization calibration. The controller outputs a calibrated image with the object bounding box, the corresponding class label, and a respective confidence score. The confidence score is a probability associated with the predicted class label. The multi-class confidence calibration is determined as a difference between a fused mean confidence and a certainty with accuracy. The fused mean confidence is between a mean logits-based class-wise confidence and class wise certainty. The controller determines the localization calibration by determining a deviation between a predicted mean bounding box overlap and a predictive certainty of the bounding box.


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    Title :

    MULTICLASS CONFIDENCE AND LOCALIZATION CALIBRATION FOR OBJECT DETECTION


    Contributors:

    Publication date :

    2025-03-13


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06V / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL



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