A novel finetuned YOLOv8 model for real-time underwater trash detection

When recognizing underwater images, problems, including poor image quality and complicated backdrops, are significant. The main problem of underwater images is the blurriness and invisibility of objects present in an image. This study presents a unique object identification design built on a YOLOv8 (You Only Look Once) framework upgraded to address these problems and further improve the models' accuracy. The study also helps in identifying underwater trash. The model is a two-phase detector model. The first phase has an Underwater Image Enhancer (UIE) data augmentation technique that works with Laplacian pyramids and gamma correctness methods to enhance the underwater images. The second phase, the proposed refined, innovative YOLOv8 model for classification purposes, takes the output from the first stage as its input. The YOLOv8 model's existing feature extractor is replaced in this study with a new feature extractor technique, HEFA, that yields superior results and better detection accuracy. The introduction of the UIE and HEFA feature extractor method represents the significant novelty of this paper. The proposed model is pruned simultaneously to eliminate unnecessary parameters and further condense the model. Pruning causes the model's accuracy to decline. Thus, the transfer learning procedure is employed to raise it. The trials’ findings show that the technique can detect objects with an accuracy of 98.5% and a mAP@50 of 98.1% and that its real-time detection speed on the GPU is double that of the YOLOv8m model's baseline performance.