Creating an Annotated Image Dataset for Wood Vessel Segmentation

• Gustavo Chagas Andrade UNIFESP
• Fabio Augusto Faria AIPS / INESC-ID / Instituto Superior Técnico Universidade de Lisboa
• Sérgio Ronaldo Barros dos Santos UNIFESP

Resumo

Quantitative Wood Anatomy (QWA) is critical for understanding plant hydraulic function, ecological strategies, and environmental responses through the analysis of xylem anatomical traits. However, the advancement of automated image analysis in QWA has been hindered by the lack of large-scale annotated datasets, limiting the effective use of deep learning (DL) techniques. Existing datasets are small, domain-specific, and lack the diversity required for robust model generalization. This work addresses these limitations by introducing a large-scale, diverse annotated dataset derived from the Inside Wood repository. To establish an optimal annotation pipeline, we evaluated multiple semantic segmentation methods, which demonstrated that they trained on our dataset achieved a mean Intersection over Union (mIoU) exceeding 90%, significantly outperforming models from scratch, pre-trained on ImageNet and fine-tuned on target-domain data for a real-world scenario. This results validate our dataset and annotation methodology as a strong foundation for developing accurate and generalizable segmentation models for practical and industrial QWA applications.

Palavras-chave: Training, Graphics, Limiting, Image analysis, Annotations, Biological system modeling, Semantic segmentation, Pipelines, Hydraulic systems, Data models