TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations

TimberVision is a dataset and framework for tree-trunk detection and tracking based on RGB images. It combines the advantages of oriented object detection and instance segmentation for optimizing robustness and efficiency, as described in the corresponding paper presented at WACV 2025. This repository contains images and annotations of the dataset as well as associated files. Source code, models, configuration files and further documentation can be found on our GitHub page. Data Structure The repository provides the following subdirectories: images: all images included in the TimberVision dataset labels: annotations corresponding to each image in YOLOv8 instance-segmentation format labels_eval: additional annotations mot: ground-truth annotations for multi-object-tracking evaluation in custom format timberseg: custom annotations for selected images from the TimberSeg dataset videos: complete video files used for evaluating multi-object-tracking (annotated keyframes sampled from each file are included in the images and labels directories) scene_parameters.csv: annotations of four scene parameters for each image describing trunk properties and context (see the paper for details) train/val/test.txt: original split files used for training, validation and testing of oriented-object-detection and instance-segmentation models with YOLOv8 sources.md: references and licenses for images used in the open-source subset Subsets TimberVision consists of multiple subsets for different application scenarios. To identify them, file names of images and annotations include the following prefixes: tvc: core dataset recorded in forests and other outdoor locations tvh: images depicting harvesting scenarios in forests with visible machinery tvl: images depicting loading scenarios in more structured environments with visible machinery tvo: a small set of third-party open-source images for evaluating generalization tvt: keyframes extracted from videos at 2 fps for tracking evaluation Citing If you use the TimberVision dataset for your research, please cite the original paper: Steininger, D., Simon, J., Trondl, A., Murschitz, M., 2025. TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).