Aiming at the problem of low visibility of underwater environment, which leads to the leakage of small target detection and low accuracy, this paper proposes an improved algorithm USSTD-YOLOv8n (Underwater small-size target detection YOLOv8n) based on YOLOv8n. First, CARAFE is adopted as anew up-sampling method to achieve more correct feature reconstruction under low underwater visibility. Second, Context Guided Block (CG block) is introduced to replace part of the convolutional structure, which makes USSTD-YOLOv8n have stronger feature extraction capability. Finally, Inner-CIoU is adopted as the loss function to improve the generalization ability of USSTD-YOLOv8n, to obtain more correct detection results. To verify the robustness and accuracy of the model, a new experimental strategy is used to perform one set of ablation experiments and three sets of comparison experiments on the URPC2018 and URPC2020 datasets, the