Underwater target detection, object classification, and image segmentation play an important role in ocean exploration and marine life studies for which the improvement of relevant technology is of much practical significance. Although existing target detection, image classification, and segmentation algorithms have achieved excellent performance on land they often fail to achieve satisfactory outcomes of detection and classification when in the underwater environment. In this paper, one of the most advanced target detection algorithms, YOLO v8 (You Only Look Once), was first applied in the underwater environment before being improved by combining it with some methods characteristic of the underwater environment. The state-of-the-art backbone and neck architectures and TC-YOLO/SAM were treated as the basic backbone network of YOLO v8, which makes the network suitable for underwater images.