Tools for ecosystem monitoring based on fish detection and classification using deep neural networks
This study explores the transformative impact of artificial intelligence (AI) in ecosystem monitoring, specifically object detection with YOLO (You Only Look Once), emphasising the search for optimal tools and model efficiency. The shift from manual counting to AI-based detection significantly reduc...
| 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:2117/411165 |
| Acceso en línea: | https://hdl.handle.net/2117/411165 https://dx.doi.org/10.5821/iwp.2024.23.14158 |
| Access Level: | acceso abierto |
| Palabra clave: | Ocean bottom -- Research Artificial intelligence -- Engineering applications Artificial intelligence Object detection Classification YOLOv8 Ecosystem monitoring Fons marins -- Investigació Intel·ligència artificial -- Aplicacions a l'enginyeria Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura |
| Sumario: | This study explores the transformative impact of artificial intelligence (AI) in ecosystem monitoring, specifically object detection with YOLO (You Only Look Once), emphasising the search for optimal tools and model efficiency. The shift from manual counting to AI-based detection significantly reduces time investment. Methodologically, the YOLO model is employed, and comprehensive training strategies are outlined. The threefold data division ensures unbiased evaluation, and diverse configurations are explored for optimal model performance. Key metrics, including IoU, Precision, Recall, and mAP, along with tools like confusion matrices, contribute to a thorough understanding of the model’s capabilities. Additionally, the model itself serves as a semi-automatic labelling tool. |
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