Multiclass Confidence and Localization Calibration for Object Detection
Pathiraja, Bimsara Gunawardhana, Malitha Khan, Muhammad Haris
Pathiraja, Bimsara
Gunawardhana, Malitha
Khan, Muhammad Haris
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Department
Computer Vision
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Patent
Date
2025
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English
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Abstract
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.
Citation
Multiclass Confidence and Localization Calibration for Object Detection, by B. Pathiraja, M. Gunawardhana, M. H. Khan. (2025, Mar. 13). Patent 20250086934 [Online]. Available: https://www.freepatentsonline.com/y2025/0086934.html
Source
US Patent App. 18/392,651
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Google Patent