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

2025-01-13

AI Summary

In autonomous forestry operations, accurate detection, part-level segmentation, and cross-frame tracking of logs and live trees remain challenging under complex forest environments using only RGB imagery. Method: This paper introduces the first RGB-only framework for single-tree geometric modeling and dynamic tracking. It (1) constructs the largest fine-grained log-part dataset to date (2,000+ images, 51,000+ pixel-level annotations); (2) designs a multi-task fusion network that unifies oriented detection and instance segmentation to produce complete trunk representations; and (3) integrates automatic geometric attribute estimation with multi-object tracking to enhance cross-frame robustness. Results: Experiments on challenging field scenarios demonstrate significant performance gains over state-of-the-art methods. The framework achieves end-to-end generation of digital single-tree representations for the first time, establishing a scalable foundation for autonomous timber harvesting and processing.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Unmanaged Forests
• Tree Part Recognition
• Timber Operations

Innovation

• TimberVision
• Tree Identification
• Environmental Robustness