Real-Time Underwater Seabed Biodiversity Detection using an Enhanced CNN-Based Deep Learning Framework
Detecting objects underwater remains a challenging problem due to light absorption, scattering effects, low visibility, color distortion, and object occlusion in complex marine environments. Recently, deep learning models based on YOLO have demonstrated promising real-time detection capabilities. However, their performance degrades in dense underwater scenes with overlapping objects. In this work, we propose YOLOv8NX, an improved underwater object detection framework. An X-Attention mechanism is integrated into the YOLOv8 architecture to enhance spatial feature discrimination and localization accuracy. The proposed model was evaluated on an open-source underwater object detection dataset containing 5,542 annotated images using YOLOv5, YOLOv7, YOLOv8, and YOLOv11. All models were trained and tested under identical experimental settings using Precision, Recall, mAP@0.5, and mAP@0.5:0.95 metrics. The experimental results demonstrate that YOLOv8NX achieved the highest precision of 83.77%, resulting in cleaner and more distinguishable bounding boxes in dense underwater scenes with overlapping objects. The proposed model demonstrated improved localization reliability and spatial separation in challenging underwater conditions, even though YOLOv8 had higher recall and mAP values. The results indicate that the proposed attention- enhanced framework can support more robust real-time underwater biodiversity monitoring and intelligent marine observation systems.