Automated mask generation for efficient YOLO-based instance segmentation in marine environments for fish detection
This paper addresses the laborious, time-consuming and error- prone process of generating ground truth data to perform instance segmentation of fish in their natural habitat. Our proposal is to use the Segment Anything Model (SAM), which allows zero- shot inference, to automatically build the segmen...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/411427 |
| Acceso en línea: | https://hdl.handle.net/2117/411427 https://dx.doi.org/10.5821/iwp.2024.23.14134 |
| Access Level: | acceso abierto |
| Palabra clave: | Animals -- Identification Machine learning Instance segmentation Deep learning SAM YOLO Animals -- Identificació Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| Sumario: | This paper addresses the laborious, time-consuming and error- prone process of generating ground truth data to perform instance segmentation of fish in their natural habitat. Our proposal is to use the Segment Anything Model (SAM), which allows zero- shot inference, to automatically build the segmentation masks, significantly reducing the dataset creation time and enhancing scalability to larger datasets. Experimental results using You Only Look Once (YOLO) demonstrate only marginal performance differences between our approach –with segmentation masks created with no human intervention– and a standard training using a fully human-labeled dataset. The results underscore the effectiveness of the automated workflow discussed herein, showcasing substantial reduction in dataset creation time, particularly in demanding underwater scenarios. |
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