A Low-Cost Machine Learning Approach for Timber Diameter Estimation

TL;DR

This paper presents a cost-effective machine learning method using YOLOv5 to accurately estimate timber log diameters from RGB images in real-world industrial settings, reducing reliance on expensive sensors and expert labor.

Contribution

It introduces a practical, scalable approach for timber diameter estimation using standard RGB images and YOLOv5, suitable for small and medium-sized wood processing facilities.

Findings

Achieved a mean Average Precision (mAP@0.5) of 0.64 in log detection.

Demonstrated reliable detection with modest computational resources.

Validated effectiveness in real-world industrial conditions.

Abstract

The wood processing industry, particularly in facilities such as sawmills and MDF production lines, requires accurate and efficient identification of species and thickness of the wood. Although traditional methods rely heavily on expert human labor, they are slow, inconsistent, and prone to error, especially when processing large volumes. This study focuses on practical and cost-effective machine learning frameworks that automate the estimation of timber log diameter using standard RGB images captured under real-world working conditions. We employ the YOLOv5 object detection algorithm, fine-tuned on a public dataset (TimberSeg 1.0), to detect individual timber logs and estimate thickness through bounding-box dimensions. Unlike previous methods that require expensive sensors or controlled environments, this model is trained on images taken in typical industrial sheds during timber delivery. Experimental results show that the model achieves a mean Average Precision (mAP@0.5) of 0.64, demonstrating reliable log detection even with modest computing resources. This lightweight, scalable solution holds promise for practical integration into existing workflows, including on-site inventory management and preliminary sorting, particularly in small and medium-sized operations.