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A Deep Learning Biomimetic Milky Way Compass.

Yiting Tao1, Michael Lucas1, Asanka Perera2

  • 1School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.

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Summary
This summary is machine-generated.

This study introduces a vision-based method using the Milky Way (MW) for insect navigation. The algorithm reliably detects heading changes, proving useful for autonomous systems.

Keywords:
Milky WayYOLOv8biomimeticinstance segmentationorientation

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Area of Science:

  • Astronomy
  • Ethology
  • Computer Vision

Background:

  • Organisms move in straight lines for survival, utilizing environmental cues for navigation.
  • Night-active insects use celestial cues, including the Milky Way (MW), for orientation.
  • Traditional image processing methods struggle to segment the MW under challenging lighting conditions.

Purpose of the Study:

  • To develop and evaluate a vision-based algorithm for detecting heading direction changes using the MW.
  • To assess the reliability of MW orientation for autonomous navigation systems.
  • To overcome limitations of existing MW segmentation techniques in varied light conditions.

Main Methods:

  • A novel algorithm combining YOLOv8m-seg and normalized second central moments to calculate MW orientation.
  • Training a custom dataset of MW images for the YOLOv8m-seg model.
  • Field trials in South Australia comparing segmented MW imagery with GPS heading data.

Main Results:

  • The YOLOv8m-seg model achieved a segmentation mAP@0.5 of 84.7% on the validation dataset.
  • The segmented MW image proved to be a reliable orientation cue for short-term navigation.
  • A small angular difference (5-10°) was observed between the proposed method and ground truth during turns.

Conclusions:

  • The proposed algorithm offers a robust method for MW orientation detection, even with significant moonlight or light pollution.
  • This technique shows significant potential for enhancing autonomous navigation systems, particularly for night-active insects.
  • The study validates the MW as a viable navigational aid for artificial systems in real-world conditions.