To address the problems of misdetection, omission detection, and low detection efficiency when detecting underwater targets due to the complex underwater environment, a lightweight underwater target detection algorithm with an improved YOLOv8 model is proposed. First, to ameliorate the problem of insufficient feature fusion in the neck network, the neck network of YOLOv8 is fused with a BiFPN bidirectional feature pyramid structure to improve the detection of the small target layer. Second, to address the problem of the large number of parameters of the convolution module in the network and high computational complexity, an Adaptive-Attention Down-Sampling(AADS) module is designed to replace the convolution module in the backbone network to reduce the number of model parameters and amount of computation. Finally, Large Separable Kernel Attention (LSKA) is introduced to strengthen the feature extraction capability such that the model can focus on important information more accurately and improve target detection accuracy. The experimental results show that in the underwater target detection dataset, the improved algorithm improves the average detection accuracy by 1.4%, reduces the number of model parameters by 43.3%, and reduces the computational complexity of the model by 15.9% when compared with YOLOv8. This realizes a good balance between detection accuracy and detection speed.