WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
This addresses the problem of automated wood cell type localization for industries and conservation efforts, representing an incremental improvement over existing methods.
The paper tackles wood species identification from microscopic images by introducing WoodYOLO, an object detector that adapts YOLO for high-resolution microscopy, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7.
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.