Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems
This work addresses the need for accurate and efficient segmentation in UAV-based forestry operations, though it is incremental as it benchmarks existing methods rather than introducing new ones.
The paper tackles the problem of tree branch segmentation for autonomous forestry systems by evaluating deep learning methods across three resolutions, finding that U-Net with MiT-B4 backbone performs strongly at lower resolutions, while U-Net+MiT-B3 and U-Net++ excel at higher resolutions, with PSPNet offering efficiency but reduced IoU by up to 25.7 percentage points.
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.