Deep learn-based object detection algorithm has two main difficulties in underwater target detection,which are limited storage and computing capabilities of underwater equipment and fuzzy underwater images and small organisms gathering.This limitation requires the un derwater target detection algorithm to be lightweight and efficient,so the existing target dete ction algorithm can not fully meet the needs of underwater target detection.In this paper,a lightweigh t one-stage detection model SG-DET is designed to improve the network framework of single shot MultiBox detector (SSD).On the one ha nd,using the idea of GhostNet for reference,ShuffleNetV2 network is reconstructed,and a new lightweight feature extraction network SGnet is proposed,which further reduce the number of model parameters and made the model size more suitable for deployment in underwater eq uipment.On the other hand,the network mainly uses six feature maps of different scales to dete ct organisms of different sizes.For this purpose,a cross-scale feature fusion module (AFF) using two-branch attention mechanism is designed. The AFF module firstly introduces the attention mechanism to weight the input fe atures in both global channel and local channel,so as to highlight useful information.To impr ove the problem of low detection accuracy caused by small and fuzzy objects,the fourth layer with a higher degree of nonlinearity is selected to enhance the semantic information of the first three layers,so that the first three layers have a better performance in identifying small objects with a small cost.The model is tested on the underwater data set UPRC provided by China Underwater Robot Compet ition,and the mAP and speed reach 71.75% and 69 FPS respectively,while the number of model p arameters is only 4.91 M.The results show that the proposed method achieves a good balance be tween precision,speed and number of parameters.