In response to the challenges of fuzzy image and numerous small targets in underwater target detection, which lead to missed detection and false detection with the YOLOv8n algorithm, we proposed an enhanced lightweight underwater target detection algorithm. Initially, within the backbone network, certain convolutions were substituted with non-strided space-to-depth convolution, and a global attention mechanism was introduced to augment global contextual information, thereby improving the network's ability to extract features from blurry and small targets. Subsequently, the conventional upsampling method was replaced with a lightweight upsampling operator, content aware reassembly of features, to broaden the model's receptive field. Furthermore, the normalized Wasserstein distance was introduced and integrated with complete intersection over union to devise a novel localization regression loss function, aimed at increasing the accuracy of small target localization in complex underwater environment. Finally, a dynamic target detection head combined with parameterized rectified linear unit was proposed to enhance the performance of the original detection head, thereby improving the model's proficiency in managing small underwater targets. Experimental results demonstrated that the improved YOLOv8n algorithm achieved a mean average precision of 86.62% on the RUOD dataset, marking a 3.20 percentage points improvement over that of the original YOLOv8n algorithm. The total number of model parameters was 5.67 M, with the number of gigabit floating-point operations is 12.5, fulfilling the criteria for lightweight model.