In the complex underwater environment, aiming at the poor detection performance of traditional YOLO target detection method, an underwater target detection model based on improved YOLO11 is proposed. Firstly, by introducing context guidance module CGBD, a multi-scale feature extractor is used to enhance the network capture capability. Secondly, in order to solve the problem that the number of parameters is too large due to feature redundancy in the network, the lightweight and efficient aggregation module RGCSPELAN is designed to reduce the burden of the model. To solve the problem that the localization and recognition ability of the original detection head is insufficient and the calculation cost is high, a lightweight and efficient DEC-Head detection head is constructed by combining the heavy parameterization strategy and detail enhancement convolution. In addition, Wise-Inner-MPD loss function is used to improve the generalization ability and accelerate the convergence of the model. The experimental results in URPC dataset show that compared with the benchmark model YOLO11, the proposed method improves the mean accuracy of mAP50 and MAP50-90 by 2.4% and 2.1% points respectively. Moreover, in the experimental results of RUOD dataset, Compared with YOLO11, the average accuracy of the improved model mAP50 increased by 1.3% and the recall rate R increased by 1.5%, showing better underwater target detection performance than other mainstream detection methods.