#5922. The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

July 2026publication date
Proposal available till 19-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Vision and Pattern Recognition;
Software;
Artificial Intelligence;
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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.
Keywords:
Anomaly detection; Autonomous driving; Out-of-distribution detection; Semantic segmentation; Uncertainty estimation

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