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

TL;DR

TimberVision introduces a large, detailed RGB image dataset and a multi-task framework for accurate log and tree detection, segmentation, and tracking to automate forestry operations safely and efficiently.

Contribution

The paper presents the TimberVision dataset and a novel multi-task framework for log-component segmentation and tracking in forestry, surpassing existing datasets in size and detail.

Findings

The dataset contains over 2,000 images with 51,000 annotated trunk components.

The framework achieves accurate detection and segmentation of logs and trees from RGB images.

Multi-object tracking enhances robustness in challenging environmental conditions.

Abstract

Timber represents an increasingly valuable and versatile resource. However, forestry operations such as harvesting, handling and measuring logs still require substantial human labor in remote environments posing significant safety risks. Progressively automating these tasks has the potential of increasing their efficiency as well as safety, but requires an accurate detection of individual logs as well as live trees and their context. Although initial approaches have been proposed for this challenging application domain, specialized data and algorithms are still too scarce to develop robust solutions. To mitigate this gap, we introduce the TimberVision dataset, consisting of more than 2k annotated RGB images containing 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. Based on this data, we conduct a series of ablation experiments for oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. We introduce a generic framework to fuse the components detected by our models for both tasks into unified trunk representations. Furthermore, we automatically derive geometric properties and apply multi-object tracking to further enhance robustness. Our detection and tracking approach provides highly descriptive and accurate trunk representations solely from RGB image data, even under challenging environmental conditions. Our solution is suitable for a wide range of application scenarios and can be readily combined with other sensor modalities.