Abstract: To solve the problem of small target detection in sonar images, which is difficult, low precision, and prone to misdetection and omission detection, this paper proposes an improved algorithm for small target detection in sonar images based on YOLOv8s. Firstly, considering that small targets in sonar images usually have low contrast and are easily overwhelmed by noise, an efficient multi-level screening feature pyramid network (EMS-FPN) is proposed. Secondly, since the classification branch and localization branch of the decoupled head are independent, which will increase the number of parameters of the model, and at the same time, it is difficult to effectively adapt to the detection needs of targets of different scales, resulting in poor detection of small targets, the task dynamic alignment detection head module (TDADH) is designed. Finally, to verify the effectiveness of the model in this paper, the corresponding validation was carried out on URPC2021 and SCTD expanded sonar dataset, mAP0.5 improved by 0.3% and 1.8% compared with YOLOv8s, respectively, and the number of parameters was reduced by 22.5%. The results show that the method proposed in this paper not only improves the accuracy but also significantly reduces the number of model parameters in the task of target detection in sonar images.