标题： 一种低成本的机器学习方法用于木材直径估计 显示英文标题

标题： A Low-Cost Machine Learning Approach for Timber Diameter Estimation

摘要： 木材加工行业，特别是在锯木厂和中密度纤维板生产线等设施中，需要准确且高效地识别木材的种类和厚度。尽管传统方法主要依赖专家人工，但它们速度慢、不一致且容易出错，尤其是在处理大量木材时。本研究专注于实用且成本效益高的机器学习框架，通过在真实工作条件下采集的标准RGB图像自动估算原木直径。我们采用在公开数据集（TimberSeg 1.0）上微调的YOLOv5目标检测算法，以检测单个原木并通过边界框尺寸估计厚度。与之前需要昂贵传感器或受控环境的方法不同，该模型是在木材运输期间在典型工业棚内拍摄的图像上训练的。实验结果表明，该模型实现了0.64的平均精度（mAP@0.5），即使在计算资源有限的情况下也能可靠地检测原木。这种轻量级、可扩展的解决方案有望实际集成到现有工作流程中，包括现场库存管理和初步分类，特别是在中小型操作中。 显示英文摘要

摘要： 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.