Abstract
Underwater environments present significant challenges for object detection due to limited visibility and inconsistent lighting. This research aims to develop a computational model to improve underwater image quality, leading to more accurate detection of aquatic organisms, specifically fish. To achieve this, we investigate the efficacy of the YOLOv8m model, a state-of-the-art deep learning architecture, for underwater object detection. The model’s performance is evaluated on a comprehensive dataset focused on fish detection. Additionally, we compare YOLOv8m’s performance against established models like Faster-RCNN and Single Shot MultiBox Detector (SSD). The results of this study demonstrate exceptional performance by the YOLOv8m model, achieving a noteworthy F1 score of 64.31%. This score suggests superior efficiency and effectiveness in underwater object detection compared to the alternative models. These findings reaffirm the potential of the proposed model for underwater object detection within aquatic environments. The impressive results highlight the model’s potential to enhance subaquatic monitoring and contribute valuable data for marine research and applications.
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The data that support the findings of this study are available on request.
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Bajpai, A., Tiwari, N., Yadav, A. et al. Enhancing Underwater Object Detection: Leveraging YOLOv8m for Improved Subaquatic Monitoring. SN COMPUT. SCI. 5, 793 (2024). https://doi.org/10.1007/s42979-024-03170-z
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DOI: https://doi.org/10.1007/s42979-024-03170-z