An improved YOLOv8 algorithm for underwater target detection is proposed to prevent missed detection of objects with different scales and overlapping occlusion. Firstly, deformable convolutions are introduced into the backbone network (deformable convolution network, DCN) to improve the feature extraction capability of the model by means of the adaptive deformation mechanism of convolution kernels. Secondly, a module combining atrous convolution and spatial pyramid, termed ASPF, is designed to expand the receptive field of the output feature map and improve the perception ability of the model for detecting underwater targets of multiple scales. Finally, the loss function is improved to optimize the training process of the model and improve detection accuracy. The improved algorithm is tested on the URPC data set, and the results show that its detection accuracy reaches 87.3%, which is 3.4% higher than that of the original YOLOv8 algorithm. Moreover, it can accurately detect underwater targets with different scales and overlapping occlusion.