Underwater Marine Species Detection Using YOLOv8
As our oceans play a crucial role in maintaining ecological balance, the need for efficient methods to monitor and study marine life becomes paramount. This paper introduces a novel approach to underwater marine species identification using YOLOv8, a state-of-the-art object detection model. Leveraging a diverse dataset of underwater imagery, this study addresses the unique challenges posed by the underwater environment, such as variable lighting conditions and complex backgrounds. We detail the methodology, encompassing dataset preparation, model configuration, and training parameters specific to underwater species identification. Our experimental results showcase the effectiveness of YOLOv8 in accurately detecting and classifying various marine species. This study marks a pioneering effort in deploying YOLOv8 for underwater marine species identification and lays the groundwork for future advancements. Additionally, the model’s architecture is designed with scalability in mind, paving the way for transfer learning to incorporate more parameters and expand the range of identified species, thus enhancing the model’s capabilities for broader applications in marine research and conservation.
