Abstract: Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles (UAVs) to autonomously extract standing tree stem attributes. Using cameras as sensors makes these UAVs compact and lightweight, facilitating safe and flexible navigation in dense forests. However, their limited onboard computational power makes real-time, image-based tree trunk segmentation challenging, emphasizing the urgent need for lightweight and efficient segmentation models. In this study, we present RT-Trunk, a model specifically designed for real-time tree trunk instance segmentation in complex forest environments. To ensure real-time performance, we selected SparseInst as the base framework. We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks, thereby improving segmentation accuracy. We further integrate the lightweight convolutional block attention module (CBAM), enabling the model to focus on tree trunk features while suppressing irrelevant information, which leads to additional gains in segmentation accuracy. To enable RT-Trunk to operate effectively under diverse complex forest environments, we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations, seasons, weather conditions, tree species, and levels of forest clutter. Compared with the other tree trunk segmentation methods, the RT-Trunk method achieved an average precision of 91.4% and the fastest inference speed of 32.9 frames per second. Overall, the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accuracy, making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes. The code for this work is available at https://github.com/NEFU-CVRG/RT-Trunk.

Key words: Tree trunk detection, Real-time instance segmentation, SparseInst, Under-canopy UAVs