# Trunk Detection in Complex Forest Environments Using a Lightweight YOLOv11-TrunkLight Algorithm

## TL;DR

This paper introduces a lightweight algorithm for detecting tree trunks in forests, enabling efficient robot navigation.

## Contribution

A novel lightweight trunk detection algorithm with reduced computational complexity and improved performance on edge devices.

## Key findings

- The proposed algorithm improves detection speed by 13.5% compared to the baseline.
- It reduces parameters by 34.6% and computational load by 39.7% with minimal loss in accuracy.
- The algorithm is suitable for resource-constrained edge devices in forestry applications.

## Abstract

The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management.