FishDet-YOLO: Enhanced Underwater Fish Detection with Richer Gradient Flow and Long-Range Dependency Capture through Mamba-C2f

The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation measures, and maintaining biodiversity. However, there are two main issues with current fish detection algorithms. First, the lighting conditions underwater are significantly different from those on land. In addition, light scattering and absorption in water trigger uneven illumination, color distortion, and reduced contrast in images. The accuracy of detection algorithms can be affected by these lighting variations. Second, the wide variation of fish species in shape, color, and size brings about some challenges. As some fish have complex textures or camouflage features, it is difficult to differentiate them using current detection algorithms. To address these issues, we propose a fish detection algorithm—FishDet-YOLO—through improvement in the YOLOv8 algorithm. To tackle the complexities of underwater environments, we design an Underwater Enhancement Module network (UEM) that can be jointly trained with YOLO. The UEM enhances the details of underwater images via end-to-end training with YOLO. To address the diversity of fish species, we leverage the Mamba model’s capability for long-distance dependencies without increasing computational complexity and integrate it with the C2f from YOLOv8 to create the Mamba-C2f. Through this design, the adaptability in handling complex fish detection tasks is improved. In addition, the RUOD and DUO public datasets are used to train and evaluate FishDet-YOLO. FishDet-YOLO achieves mAP scores of 89.5% and 88.8% on the test sets of RUOD and DUO, respectively, marking an improvement of 8% and 8.2% over YOLOv8. It also surpasses recent state-of-the-art general object detection and underwater fish detection algorithms.