The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
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.
PDF Abstract
Datasets
Introduced in the Paper:
Fishyscapes
Used in the Paper:
Cityscapes
Foggy Cityscapes
Mapillary Vistas Dataset
Lost and Found
WildDash
Results from the Paper
Ranked #13 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
