This study addresses the global issue of marine pollution, with a particular focus on plastic bag contamination, by leveraging real-time object detection techniques powered by deep learning algorithms. A detailed comparison was carried out between the YOLOv8, YOLO-NAS, and RT-DETR models to assess their effectiveness in detecting plastic waste in underwater environments. The methodology encompassed several key stages, including data preprocessing, model implementation, and training through transfer learning. Evaluation was conducted using a simulated video environment, followed by an in-depth comparison of the results. Performance assessment was based on critical metrics such as mean average precision (mAP), recall, and inference time. The YOLOv8 model achieved an mAP50 of 0.921 on the validation dataset, along with a recall of 0.829 and an inference time of 14.1 milliseconds. The YOLO-N...

This study addresses the global issue of marine pollution, with a particular focus on plastic bag contamination, by leveraging real-time object detection techniques powered by deep learning algorithms. A detailed comparison was carried out between the YOLOv8, YOLO-NAS, and RT-DETR models to assess their effectiveness in detecting plastic waste in underwater environments. The methodology encompassed several key stages, including data preprocessing, model implementation, and training through transfer learning. Evaluation was conducted using a simulated video environment, followed by an in-depth comparison of the results. Performance assessment was based on critical metrics such as mean average precision (mAP), recall, and inference time. The YOLOv8 model achieved an mAP50 of 0.921 on the validation dataset, along with a recall of 0.829 and an inference time of 14.1 milliseconds. The YOLO-N...