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Research on Orchard Navigation Line Recognition Method Based on U-Net.

Ning Xu1,2,3, Xiangsen Ning4, Aijuan Li4

  • 1College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.

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Summary
This summary is machine-generated.

This study introduces an improved U-Net model for orchard navigation line recognition, enhancing drivable area segmentation accuracy. The method effectively extracts navigation lines, offering a reliable solution for visual autonomous navigation in complex orchard environments.

Keywords:
U-Net networkattention mechanismdivisible driving areanavigation lineorchard environment

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Area of Science:

  • Computer Vision
  • Robotics
  • Agricultural Technology

Background:

  • Orchard visual navigation faces challenges from complex backgrounds and interference.
  • Existing methods struggle with accurate drivable area segmentation and navigation line extraction.

Purpose of the Study:

  • To develop an enhanced U-Net based method for robust orchard navigation line recognition.
  • To improve the accuracy and reliability of visual navigation systems in agricultural settings.

Main Methods:

  • A U-Net semantic segmentation model was improved with Spatial Attention (SA) and Coordinate Attention (CA) mechanisms.
  • An orchard dataset was created and used to train the enhanced model for drivable area segmentation.
  • Navigation lines were extracted from segmentation masks using geometric center point calculation and spline interpolation.

Main Results:

  • The enhanced model achieved high segmentation accuracy with Recall of 90.23%, Precision of 91.71%, mPA of 87.75%, and mIoU of 84.84%.
  • The extracted navigation line had an average distance error of 56 mm compared to the actual center line.
  • Performance was validated against U-Net, SegViT, SE-Net, and DeepLabv3+.

Conclusions:

  • The proposed U-Net based method significantly improves drivable area segmentation and navigation line extraction in orchards.
  • This approach provides an effective reference for visual autonomous navigation systems in challenging agricultural environments.