针对显微木纤维图像，提出高效识别树种的检测算法
WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
- 改进YOLO架构，适配高分辨率显微图像
- 在F2分数上比YOLOv10提升12.9%，比YOLOv7提升6.5%
- 适合林业监管、可持续采伐与生物多样性保护场景
木材物种识别在多个领域至关重要，从确保木材产品合法性到推动生态保护。本文提出WoodYOLO，一种专为显微木纤维分析设计的新一代目标检测算法。该方法对YOLO架构进行改进，以应对大尺寸高分辨率显微图像带来的挑战，并实现对目标细胞类型（导管分子）的高召回率定位。实验结果表明，WoodYOLO显著优于现有先进模型，在F2分数上相较YOLOv10提升12.9%，相较YOLOv7提升6.5%。这一自动化细胞类型定位能力的提升，有助于加强法规合规性，支持可持续林业实践，并推动全球生物多样性保护。
原文摘要 · Abstract (English)
Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally.
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