Research on Underwater Small Target Detection Technology Based on Single-Stage USSTD-YOLOv8n

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 mAP @ 0.5 was 0.7670,0.7910 and 0.7044, compared to the YOLOv8n algorithm, map@0.5 increased 0.0260, 0.008 and 0.007. It is proved through four sets of experiments that USSTD-YOLOv8n has better detection performance in underwater small target detection task.