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Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Florian Heidecker
Abdul Hannan
Maarten Bieshaar
Bernhard Sick
Published in
Pattern Recognition. ICPR International Workshops and Challenges
Publisher
Springer International Publishing

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Abstract

The chapter delves into the critical issue of corner case detection in autonomous driving, focusing on the uncertainty modeling of instance segmentation networks. It introduces a novel method using Monte-Carlo dropout to estimate epistemic uncertainty, enabling the detection of challenging scenarios that are difficult to classify or underrepresented in training data. The approach involves extending the architecture of Mask R-CNN with dropout layers to model both position and class uncertainties, and then clustering the predictions to detect corner cases. The chapter also highlights the importance of iteratively improving the model by incorporating these corner cases into future training datasets. By addressing the limitations of current methods and presenting a powerful, yet simple approach, this chapter offers valuable insights for researchers and practitioners in the field of autonomous driving and machine learning.
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This summary of the content was generated with the help of AI.

F. Heidecker and A. Hannan—Contributed equally to this work.

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previous chapter Input-Aware Neural Knowledge Tracing Machine
next chapter Intelligent and Interactive Video Annotation for Instance Segmentation Using Siamese Neural Networks
Title
Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks
Authors
Florian Heidecker
Abdul Hannan
Maarten Bieshaar
Bernhard Sick
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68798-4

Electronic ISBN: 978-3-030-68799-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68799-1_26
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