Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation
arxiv(2024)
摘要
The operating environment of a highly automated vehicle is subject to change,
e.g., weather, illumination, or the scenario containing different objects and
other participants in which the highly automated vehicle has to navigate its
passengers safely. These situations must be considered when developing and
validating highly automated driving functions. This already poses a problem for
training and evaluating deep learning models because without the costly
labeling of thousands of recordings, not knowing whether the data contains
relevant, interesting data for further model training, it is a guess under
which conditions and situations the model performs poorly. For this purpose, we
present corner case criteria based on the predictive uncertainty. With our
corner case criteria, we are able to detect uncertainty-based corner cases of
an object instance segmentation model without relying on ground truth (GT)
data. We evaluated each corner case criterion using the COCO and the NuImages
dataset to analyze the potential of our approach. We also provide a corner case
decision function that allows us to distinguish each object into True Positive
(TP), localization and/or classification corner case, or False Positive (FP).
We also present our first results of an iterative training cycle that
outperforms the baseline and where the data added to the training dataset is
selected based on the corner case decision function.
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