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

Source: [A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios](<https://www.sota2.com/research/paper/68fa6e411e209131cea3ebdb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:53:04.613Z

Authors: Kai A. Metzger, Peter Mortimer, Hans-Joachim Wuensche

Published: 2021-03-24T11:30:43.000Z

## Abstract

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. The dataset and pretrained model are available at mucar3.de/icpr2020-tas500.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.13109>)

[PDF](<https://arxiv.org/pdf/2103.13109.pdf>)