Automatic Pith Detection in Tree Cross-Section Images Using Deep Learning

TL;DR

This paper evaluates various deep learning models for automating pith detection in tree cross-section images, demonstrating high accuracy with Swin Transformer and improvements with NMS for Mask R-CNN.

Contribution

It provides a comprehensive comparison of deep learning models for pith detection, introducing data augmentation and NMS techniques to enhance model performance and generalizability.

Findings

Swin Transformer achieved 0.94 accuracy in segmentation.

NMS significantly improved Mask R-CNN's IoU from 0.45 to 0.80.

Deep learning models show promise for automating pith detection tasks.

Abstract

Pith detection in tree cross-sections is essential for forestry and wood quality analysis but remains a manual, error-prone task. This study evaluates deep learning models -- YOLOv9, U-Net, Swin Transformer, DeepLabV3, and Mask R-CNN -- to automate the process efficiently. A dataset of 582 labeled images was dynamically augmented to improve generalization. Swin Transformer achieved the highest accuracy (0.94), excelling in fine segmentation. YOLOv9 performed well for bounding box detection but struggled with boundary precision. U-Net was effective for structured patterns, while DeepLabV3 captured multi-scale features with slight boundary imprecision. Mask R-CNN initially underperformed due to overlapping detections, but applying Non-Maximum Suppression (NMS) improved its IoU from 0.45 to 0.80. Generalizability was next tested using an oak dataset of 11 images from Oregon State University's Tree Ring Lab. Additionally, for exploratory analysis purposes, an additional dataset of 64 labeled tree cross-sections was used to train the worst-performing model to see if this would improve its performance generalizing to the unseen oak dataset. Key challenges included tensor mismatches and boundary inconsistencies, addressed through hyperparameter tuning and augmentation. Our results highlight deep learning's potential for tree cross-section pith detection, with model choice depending on dataset characteristics and application needs.