With the growing importance of marine resources and the increasing demand for exploration of underwater environments, underwater target detection technology has become one of the key technologies in the fields of ocean engineering, underwater archaeology, and intelligent agriculture. However, due to the complexity and uncertainty of underwater environments, such as light attenuation, water turbidity, and dynamic changes of water currents, current target detection methods often perform poorly in underwater scenes. To solve the corresponding problems, this paper proposes the YOLOv5_OD_Conv model, which aims to improve the accuracy and generalisation ability of the model by introducing the OD_Conv full-dimensional dynamic convolution in the YOLOv5 Neck part. Simulation and experimental results show that the proposed method increases the detection accuracy P by 1.05%, the precision mAP0.5 by 1.5%, and the recall R by 0.43% compared to YOLOv5s. The improvement of detection effect is obvious, which proves the effectiveness of the method.