In this paper, we propose the first Test-Time Augmentation (TTA) method to estimate uncertainty and improve accuracy of pre-trained object detectors in the autonomous driving domain. We show, in an autonomous driving dataset, that even simple color-based augmentations are able to improve mean Average Precision (mAP) performance with respect to other state-of-the-art methods with the same purpose (Monte Carlo (MC) Dropout and Output Redundancy). Furthermore, we show that the quality of the estimated uncertainty and distributions can be improved, both in our method and in the state-of-the-art, if some of the parameters of the methods (bounding box selection and clustering criteria) are independently tuned for the classification and the localization subtasks of object detection.
Quantifying Object Detection Uncertainty in Autonomous Driving with Test-Time Augmentation
2023-06-04
1253154 byte
Conference paper
Electronic Resource
English
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