基于改进YOLOv8的树木年轮实例分割及信息检测方法

王品博; 王雨晨; 王双永; 周海宾

doi:10.12326/j.2096-9694.2025008

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基于改进YOLOv8的树木年轮实例分割及信息检测方法
Study on Tree Ring Instance Segmentation and Information Detection Based on Improved YOLOv8
1.中国林业科学研究院木材工业研究所，北京 100091
2.南京林业大学，江苏南京 210037
3.古建筑木材科学研究与保护国家文物局重点科研基地，北京 100091
2025年39卷第3期页码：7-18
DOI： 10.12326/j.2096-9694.2025008
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王品博,王雨晨,王双永等.基于改进YOLOv8的树木年轮实例分割及信息检测方法[J].木材科学与技术,2025,39(03):7-18. DOI： 10.12326/j.2096-9694.2025008.

WANG Pinbo,WANG Yuchen,WANG Shuangyong,et al.Study on Tree Ring Instance Segmentation and Information Detection Based on Improved YOLOv8[J].Chinese Journal of Wood Science and Technology,2025,39(03):7-18. DOI： 10.12326/j.2096-9694.2025008.

摘要

在气候学、生态学、考古学等诸多领域，树木年轮蕴含的环境变迁与历史演替信息具有不可替代的研究价值。为解决传统年轮检测方法存在效率低、易受人为因素干扰等问题，研究提出DCW-YOLOv8年轮实例分割模型，运用可扩张残差（Dilation-wise Residual，DWR）注意力模块、轻量级通用上采样算子（Content-Aware ReAssembly of Features，CARAFE）、动态非单调聚焦机制边界框损失函数（Weighted Interpolation of Sequential Evidence for Intersection over Union，Wise-IoU）组合改进，并设计一种依据模型掩码获取年轮数量与宽度信息的检测方法。消融试验和前沿模型对比试验结果表明，DCW-YOLOv8模型掩码平均精度mAP@0.50、mAP@0.50∶0.95提升，达到86.4%、53.6%，优于同类算法；注意力可视化比较结果表明，DCW-YOLOv8对年轮特征注意力更强；年轮信息检测结果表明，年轮数量检测准确率达到86.2%，年轮宽度检测误差在±0.5 mm范围内的占70%，总体平均误差为0.295 mm。研究提出的DCW-YOLOv8模型为年轮自动化检测提供新的思路和方法。

Abstract

In climatology

ecology

and archaeology

tree rings hold irreplaceable value for studying environmental change and historical succession. To improve the efficiency and reduce human interference in traditional tree-ring detection

this study proposes DCW-YOLOv8

an instance segmentation model enhanced with dilation-wise residual (DWR) attention module

lightweight CARAFE upsampling

and dynamic Wise-IoU loss. A detection method using model-generated masks is designed to quantify ring counts and widths. The ablation and comparative experiments show DCW-YOLOv8 achieves superior mask mAP (86.4% for mAP@0.50

53.6% for mAP@0.50∶0.95) versus state-of-the-art models. Attention visualization confirms stronger focus on tree-ring features. Detection results include 86.2% count-accuracy

70% width measurements within ±0.5 mm error

and a mean width error of 0.295 mm. This model provides a novel automated approach for tree-ring analysis.

关键词

Keywords

references

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