Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems

TL;DR

This study evaluates various deep learning models for tree branch segmentation in autonomous forestry, analyzing their performance across multiple resolutions and establishing benchmarks for accuracy and efficiency.

Contribution

It provides a comprehensive comparison of deep learning methods for tree branch segmentation at different resolutions, highlighting optimal configurations and efficiency trade-offs.

Findings

U-Net with MiT-B4 performs best at 256x256 resolution.

MiT-B4 leads in IoU, Dice, TS-IoU, and Boundary-F1 at 512x512.

U-Net+MiT-B3 and U-Net++ excel at 1024x1024 in validation performance and boundary quality.

Abstract

UAV-based autonomous forestry operations require rapid and precise tree branch segmentation for safe navigation and automated pruning across varying pixel resolutions and operational conditions. We evaluate different deep learning methods at three resolutions (256x256, 512x512, 1024x1024) using the Urban Street Tree Dataset, employing standard metrics (IoU, Dice) and specialized measures including Thin Structure IoU (TS-IoU) and Connectivity Preservation Rate (CPR). Among 22 configurations tested, U-Net with MiT-B4 backbone achieves strong performance at 256x256. At 512x512, MiT-B4 leads in IoU, Dice, TS-IoU, and Boundary-F1. At 1024x1024, U-Net+MiT-B3 shows the best validation performance for IoU/Dice and precision, while U-Net++ excels in boundary quality. PSPNet provides the most efficient option (2.36/9.43/37.74 GFLOPs) with 25.7/19.6/11.8 percentage point IoU reductions compared to top performers at respective resolutions. These results establish multi-resolution benchmarks for accuracy-efficiency trade-offs in embedded forestry systems. Implementation is available at https://github.com/BennyLinntu/PerformanceTreeBranchSegmentation.