A Deep Learning Biomimetic Milky Way Compass
Moving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using the Milky Way (MW), o...
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MDPI AG
2024-10-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/10/620 |
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| author | Yiting Tao Michael Lucas Asanka Perera Samuel Teague Timothy McIntyre Titilayo Ogunwa Eric Warrant Javaan Chahl |
| author_facet | Yiting Tao Michael Lucas Asanka Perera Samuel Teague Timothy McIntyre Titilayo Ogunwa Eric Warrant Javaan Chahl |
| author_sort | Yiting Tao |
| collection | DOAJ |
| description | Moving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using the Milky Way (MW), one of the navigational cues that are known to be used by night-active insects. An algorithm is proposed that combines the YOLOv8m-seg model and normalised second central moments to calculate the MW orientation angle. This method addresses many likely scenarios where segmentation of the MW from the background by image thresholding or edge detection is not applicable, such as when the moon is substantial or when anthropogenic light is present. The proposed YOLOv8m-seg model achieves a segment mAP@0.5 of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the validation dataset using our own training dataset of MW images. To explore its potential role in autonomous system applications, we compare night sky imagery and GPS heading data from a field trial in rural South Australia. The comparison results show that for short-term navigation, the segmented MW image can be used as a reliable orientation cue. There is a difference of roughly 5–10° between the proposed method and GT as the path involves left or right 90° turns at certain locations. |
| format | Article |
| id | doaj-art-4f1aaf1ece5f4eaeba97774ed65edcc9 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-4f1aaf1ece5f4eaeba97774ed65edcc92025-08-20T02:10:58ZengMDPI AGBiomimetics2313-76732024-10-0191062010.3390/biomimetics9100620A Deep Learning Biomimetic Milky Way CompassYiting Tao0Michael Lucas1Asanka Perera2Samuel Teague3Timothy McIntyre4Titilayo Ogunwa5Eric Warrant6Javaan Chahl7School of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaLund Vision Group, Department of Biology, University of Lund, 22100 Lund, SwedenSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaMoving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using the Milky Way (MW), one of the navigational cues that are known to be used by night-active insects. An algorithm is proposed that combines the YOLOv8m-seg model and normalised second central moments to calculate the MW orientation angle. This method addresses many likely scenarios where segmentation of the MW from the background by image thresholding or edge detection is not applicable, such as when the moon is substantial or when anthropogenic light is present. The proposed YOLOv8m-seg model achieves a segment mAP@0.5 of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>84.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the validation dataset using our own training dataset of MW images. To explore its potential role in autonomous system applications, we compare night sky imagery and GPS heading data from a field trial in rural South Australia. The comparison results show that for short-term navigation, the segmented MW image can be used as a reliable orientation cue. There is a difference of roughly 5–10° between the proposed method and GT as the path involves left or right 90° turns at certain locations.https://www.mdpi.com/2313-7673/9/10/620biomimeticMilky WayYOLOv8instance segmentationorientation |
| spellingShingle | Yiting Tao Michael Lucas Asanka Perera Samuel Teague Timothy McIntyre Titilayo Ogunwa Eric Warrant Javaan Chahl A Deep Learning Biomimetic Milky Way Compass Biomimetics biomimetic Milky Way YOLOv8 instance segmentation orientation |
| title | A Deep Learning Biomimetic Milky Way Compass |
| title_full | A Deep Learning Biomimetic Milky Way Compass |
| title_fullStr | A Deep Learning Biomimetic Milky Way Compass |
| title_full_unstemmed | A Deep Learning Biomimetic Milky Way Compass |
| title_short | A Deep Learning Biomimetic Milky Way Compass |
| title_sort | deep learning biomimetic milky way compass |
| topic | biomimetic Milky Way YOLOv8 instance segmentation orientation |
| url | https://www.mdpi.com/2313-7673/9/10/620 |
| work_keys_str_mv | AT yitingtao adeeplearningbiomimeticmilkywaycompass AT michaellucas adeeplearningbiomimeticmilkywaycompass AT asankaperera adeeplearningbiomimeticmilkywaycompass AT samuelteague adeeplearningbiomimeticmilkywaycompass AT timothymcintyre adeeplearningbiomimeticmilkywaycompass AT titilayoogunwa adeeplearningbiomimeticmilkywaycompass AT ericwarrant adeeplearningbiomimeticmilkywaycompass AT javaanchahl adeeplearningbiomimeticmilkywaycompass AT yitingtao deeplearningbiomimeticmilkywaycompass AT michaellucas deeplearningbiomimeticmilkywaycompass AT asankaperera deeplearningbiomimeticmilkywaycompass AT samuelteague deeplearningbiomimeticmilkywaycompass AT timothymcintyre deeplearningbiomimeticmilkywaycompass AT titilayoogunwa deeplearningbiomimeticmilkywaycompass AT ericwarrant deeplearningbiomimeticmilkywaycompass AT javaanchahl deeplearningbiomimeticmilkywaycompass |