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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，并设计了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%和53.6%，优于同类算法；注意力可视比较结果表明，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±0.5mm范围内的占76%，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。
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@50
53.6% for mAP@50：95) versus state-of-the-art models. Attention visualization confirms stronger focus on tree-ring features. Detection results include 86.2% count-accuracy
76% width measurements within ±0.5mm error
and a mean width error of 0.295mm. This model provides a novel automated approach for tree-ring analysis.
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