The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
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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 and covers the detection of both out-of-distribution objects and misclassifications. We adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. A thorough evaluation of these methods reveals a clear gap to their alleged capabilities. Our results show that failure detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.
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