Open access
Date
2021Type
- Journal Article
Abstract
Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for anomaly detection in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects. We adapt state-of-the-art methods to recent semantic segmentation models and compare uncertainty estimation approaches based on softmax confidence, Bayesian learning, density estimation, image resynthesis, as well as supervised anomaly detection methods. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art. Results, data and submission information can be found at https://fishyscapes.com/. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000511402Publication status
publishedExternal links
Journal / series
International Journal of Computer VisionVolume
Pages / Article No.
Publisher
SpringerSubject
Semantic segmentation; Anomaly detection; Uncertainty estimation; Out-of-distribution detection; Autonomous drivingOrganisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
Related publications and datasets
Is part of: https://doi.org/10.3929/ethz-b-000585663
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