Traditional algorithms rely on the precise separation and information extraction of infrared ship targets and environmental backgrounds,and it is difficult to meet the needs of ship target detection under complex background and environments with noise and other interference.To solve the problem,an infrared ship target detection algorithm based on the improved YOLOv5 is proposed.The Reasoning layer is added to YOLOv5 network,and a new architecture is used to extract the semantic relationship between image regions to predict the bounding box and class probability,which improves model detection accuracy.At the same time,the loss function of the YOLOv5 target detection network is improved to further improve the detection accuracy of the model.The verification results show that the model trained by the improved YOLOv5 algorithm has significantly improved detection accuracy and speed in comparison with several target detection algorithms listed in the experiment.The mean Average Precision (mAP) can reach 94.65%.The model has been verified that it can meet the real-time requirements and ensure high accuracy.