The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
This addresses safety-critical needs in autonomous driving by providing a benchmark to measure advancements in anomaly detection, though it is incremental as it adapts existing methods to a new task.
The authors tackled the problem of uncertainty estimation in semantic segmentation for autonomous driving by introducing the Fishyscapes benchmark, which evaluates methods for detecting anomalous objects, and found that current approaches are insufficient even in ordinary situations.
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.