Exploring Deep Learning for Underwater Plastic Debris Detection and Monitoring

In this paper, a comparative evaluation of state-of-the-art deep learning models for object detection in underwater environments focusing on marine debris detection was presented. The performance of four prominent object detection models was investigated, including: Faster R-CNN, SSD, YOLOv8, and YOLOv9, using two different datasets: TrashCAN and DeepTrash. Through quantitative analysis, the accuracy, precision, recall, and mean average precision (mAP) of each model across different object classes and environmental conditions were evaluated. The obtained results show that YOLOv9 consistently outperforms the other models, demonstrating superior precision, recall, and mAP values on both datasets. Furthermore, the stability and convergence behavior of the models during training were analyzed, highlighting the excellent stability and adaptability of YOLOv9. The obtained results underscore the effectiveness of deep learning-based approaches in marine debris detection and highlight the potential of YOLOv9 as a robust solution for environmental monitoring and intervention efforts in underwater ecosystems.