A Comprehensive Study On Underwater Object Detection Using Deep Neural Networks

The escalating crisis of marine pollution, especially the buildup of underwater waste, poses a significant threat to ocean ecosystems and marine life. Identifying and removing submerged debris is challenging due to poor visibility, complex water conditions, and the inefficiency of traditional manual approaches. This study leverages deep learning-based object detection techniques to improve underwater waste identification, focusing on the YOLOv8 and YOLOv9 architectures. Using the TrashCan 1.0 dataset—a large collection of labeled underwater images—the models were trained and evaluated based on key metrics such as mean Average Precision (mAP) and Intersection over Union (IoU). Results show that the YOLOv9c model performed best on the 3-Class dataset, achieving a mAP50:95 of 0.759, a mAP50 of 0.934, precision of 0.915, and recall of 0.877, highlighting its high accuracy and robustness. Meanwhile, YOLOv8l showed slightly better results on the 4-Class dataset with a mAP50:95 of 0.725, indicating dataset-dependent performance variations. The ability of these models to deliver real-time detection makes them ideal for integration into autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), enabling large-scale monitoring and cleanup operations. By advancing AI-driven solutions for marine conservation, this research contributes to more efficient and scalable efforts in mitigating the long-term impact of ocean pollution.