用普通摄像头和轻量模型实现木材直径自动估算，适合中小厂直接落地。
A Low-Cost Machine Learning Approach for Timber Diameter Estimation
- 基于YOLOv5和标准RGB图像，无需昂贵传感器
- 在真实车间环境下实现[email protected]达0.64的检测精度
- 适合小中型企业用于现场库存与分拣，部署成本低
木材加工业（如锯木厂和MDF生产线）需要高效准确地识别木材种类和厚度。传统依赖人工的方法速度慢、不一致且易出错，尤其在大批量处理时。本研究提出一种低成本机器学习框架，利用实际工作环境中拍摄的标准RGB图像，自动估算原木直径。采用经过公开数据集TimberSeg 1.0微调的YOLOv5目标检测算法，通过边界框尺寸估算木材厚度。与以往需昂贵传感器或受控环境的方法不同，该模型在典型车间内木材交付过程中采集的图像上训练。实验表明，模型在真实场景下达到[email protected]为0.64，具备可靠检测能力，且仅需较低算力。该轻量、可扩展的方案有望直接集成到现有流程中，适用于现场库存管理与初步分拣，尤其适合中小型生产单位。
原文摘要 · Abstract (English)
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 ([email protected]) 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.
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