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

Siqi Zhang1, Yubi Zheng2, Rengui Bi3

  • 1College of Physics, Mechanical and Electrical Engineering, Jishou University, Jishou 416000, China.

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|October 16, 2025
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Summary
This summary is machine-generated.

This study introduces YOLOv11-TrunkLight, a lightweight algorithm for accurate tree trunk detection in forests. It enhances robot navigation on edge devices by improving speed and reducing computational load.

Keywords:
EffiDetStarNetYOLOv11 lightweight modeldual-path feature decouplinginspection robot

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Area of Science:

  • Computer Vision
  • Robotics
  • Forestry Technology

Background:

  • Accurate tree trunk detection is crucial for autonomous robot navigation in complex forest environments.
  • Existing detection models face challenges in balancing accuracy and real-time performance on resource-constrained edge devices.

Purpose of the Study:

  • To propose a lightweight algorithm, YOLOv11-TrunkLight, for efficient and accurate trunk detection on edge devices.
  • To enhance the capabilities of inspection robots for intelligent forestry management.

Main Methods:

  • Developed a novel StarNet_Trunk backbone network utilizing element-wise multiplication and depthwise separable convolutions.
  • Integrated the C2DA deformable attention module to address geometric deformation of tree trunks.
  • Employed an EffiDet detection head with dual-path feature decoupling and a dynamic anchor mechanism for improved efficiency.

Main Results:

  • YOLOv11-TrunkLight achieved a 13.5% increase in detection speed compared to the baseline YOLOv11.
  • Reduced the number of parameters by 34.6% and computational load (FLOPs) by 39.7%.
  • Maintained a marginal decrease of only 0.1% in mean average precision (mAP).

Conclusions:

  • YOLOv11-TrunkLight offers a significant improvement in efficiency for trunk detection on resource-constrained edge devices.
  • The algorithm provides a viable solution for enhancing autonomous navigation and supporting intelligent forestry management.