Automated underwater character recognition technology can more efficiently locate and track underwater equipment through numbers,which is the key to managing and maintaining underwater equipment.In view of the problems such as the small target difference of the task and interference in the underwater scene,and considering its detection speed requirements,this article proposes a lightweight improved model based on the YOLOv7-tiny model. First,MobileNetV3 is used as a new feature extraction network to lightweight the overall framework.Then PConv is introduced into the ELAN module to reduce the calculation amount of the Neck layer.Finally,the displacement attention mechanism is applied to the Head layer to improve the model's ability to position characters.expression ability. Experimental results show that compared with the original model,the mAP of the improved model is increased by 2.4%,the amount of parameters and calculations are reduced by 30.0%and 38.5%respectively,and the detection speed is increased by 30.8%.The improved model has higher efficiency and accuracy in underwater character recognition tasks,providing feasibility for promoting and realizing the deployment of underwater automated identification and numbering equipment.