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StreetHazards Dataset

A benchmark for anomaly segmentation

StreetHazards Dataset

Detecting out-of-distribution examples is important for safety-critical machine learning applications such as self-driving vehicles. However, existing research mainly focuses on small-scale images where the whole image is considered anomalous. We propose to segment only the anomalous regions within an image, and hence we introduce the Combined Anomalous Object Segmentation benchmark for the more realistic task of large-scale anomaly segmentation. Our benchmark combines two novel datasets for anomaly segmentation that incorporate both realism and anomaly diversity. Using both real images and those from a simulated driving environment, we ensure the background context and a wide variety of anomalous objects are naturally integrated, unlike before.

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UC Berkeley
https://www.berkeley.edu
Task
Semantic Segmentation
Annotation Types
Semantic Segmentation
7000
Items
18
Classes
7000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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