# The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

## TL;DR

Fishyscapes introduces a benchmark for evaluating uncertainty estimation in semantic segmentation for urban driving, highlighting current limitations in detecting anomalies and guiding future improvements.

## Contribution

It provides the first comprehensive benchmark for uncertainty estimation in real-world semantic segmentation, focusing on anomaly detection in autonomous driving scenarios.

## Key findings

- Anomaly detection remains challenging in ordinary situations.
- State-of-the-art methods show significant room for improvement.
- The benchmark enables measuring progress beyond current techniques.

## 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 uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We~adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. 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.