In recent years, underwater target detection algorithms based on deep learning have greatly promoted the development of the field of marine science and underwater robotics. However, due to the complexity of the underwater environment, there are problems, such as target occlusion, overlap, background confusion, and small object, that lead to detection difficulties. To address this issue, this paper proposes an improved underwater target detection method based on YOLOv8s. First, a lightweight backbone network with efficient transformers is used to replace the original backbone network, which enhances the contextual feature extraction capability. Second, an improved bidirectional feature pyramid network is used in the later multi-scale fusion part by increasing the input of bottom-level information while reducing the model size and number of parameters. Finally, a dynamic head with an attention mechanism is introduced into the detection head to enhance the classification and localization of small and fuzzy targets. Experimental results show that the proposed method improves the mAP0.5:0.95 of 65.7%, 63.7%, and 51.2% with YOLOv8s to that of 69.2%, 66.8%, and 54.8%, on three public underwater datasets, DUO, RUOD, and URPC2020, respectively. Additionally, compared with the YOLOv8s model, the model size decreased from 21.46 to 15.56 MB, and the number of parameters decreased from 11.1 to 7.9 M.