A Fine-Grained Dataset and its Efficient Semantic
Segmentation for Unstructured Driving Scenarios

Kai A. Metzger
Peter Mortimer
Hans-Joachim Wuensche

Institute for Autonomous Systems Technology
Bundeswehr University Munich
In ICPR, 2020

Overview. Example images from the TAS500 dataset.
The TAS500 dataset contains fine-grained terrain and vegetation annotations
of over 500 scenes in unstructured environments.

Research in autonomous driving for unstructured environments suffers from a lack of semantically labeled datasets compared to its urban counterpart. Urban and unstructured outdoor environments are challenging due to the varying lighting and weather conditions during a day and across seasons. In this paper, we introduce TAS500, a novel semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively. We evaluate the performance of modern semantic segmentation models with an additional focus on their efficiency. Our experiments demonstrate the advantages of fine-grained semantic classes to improve the overall prediction accuracy, especially along the class boundaries.



Metzger, Mortimer, Wuensche.

A Fine-Grained Dataset and its Efficient Semantic
Segmentation for Unstructured Driving Scenarios

ICPR, 2020.



Class distribution in the TAS500 dataset. Number of fine-grained pixels (y-axis) per class and their associated category (x-axis).

Data collection pipeline. The data was collected using the autonomous vehicle MuCAR-3. We recorded data with a frame rate of 10 Hz and cut off most of the sky and ego vehicle hood from all images. The final images have a resolution of 620px x 2026px. Our label rate amounts to around 0.1 Hz, and we consequently provide a pixel-wise semantic mask for every hundredth recorded image.


The TAS500 dataset is copyrighted and published under the Attribution-NonCommercial-ShareAlike 3.0 license. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.

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The authors gratefully acknowledge funding by the Federal Office of Bundeswehr Equipment, Information Technology and In-Service Support (BAAINBw). This webpage template is based on the Factored3D project page.