Description
The TimberVision dataset consists of more than 2k annotated RGB images and contains a total of 51k trunk components including cut and lateral surfaces, thereby surpassing any existing dataset in this domain in terms of both quantity and detail by a large margin. The dataset can be used to train oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. Additionally, a generic framework is provided to fuse the components detected by the models for both tasks into unified trunk representations. Furthermore, geometric properties are derived automatically and multi-object tracking is applied to further enhance robustness.
| Date made available | 6 Feb 2025 |
|---|---|
| Temporal coverage | 2021 - 2024 |
| Date of data production | 2021 - 2024 |
Research Field
- Assistive and Autonomous Systems
Research output
- 1 Conference Proceedings with Poster Presentation
-
TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations
Steininger, D., Simon, J., Trondl, A. & Murschitz, M., 28 Feb 2025, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 18 p.Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Poster Presentation › peer-review
Open Access
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