WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images

TL;DR

WoodYOLO is a specialized object detection model tailored for microscopic wood fiber images, significantly improving accuracy in identifying wood cell types, which benefits ecological and industrial applications.

Contribution

The paper introduces WoodYOLO, a novel adaptation of YOLO architecture optimized for microscopic wood fiber analysis and high-resolution images.

Findings

Achieves 12.9% higher F2 score than YOLOv10

Achieves 6.5% higher F2 score than YOLOv7

Outperforms existing models in wood cell localization

Abstract

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.