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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12659 |
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| _version_ | 1866910612347420672 |
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| author | Batista, Luis Felipe Wolf Khazem, Salim Adibi, Mehran Hutchinson, Seth Pradalier, Cedric |
| author_facet | Batista, Luis Felipe Wolf Khazem, Salim Adibi, Mehran Hutchinson, Seth Pradalier, Cedric |
| contents | Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12659 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects Batista, Luis Felipe Wolf Khazem, Salim Adibi, Mehran Hutchinson, Seth Pradalier, Cedric Computer Vision and Pattern Recognition Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024. |
| title | PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.12659 |