Aiming at the problems of false detection and missed detection in the infrared maritime ship image detection in the scene of near-shore dense, far-sea small target and low-resolution, as well as making the model lighter, an improved infrared maritime ship detection algorithm based on YOLOv7 is proposed. In order to enhance the feature extraction capability of the backbone network, REP-DSConv-ELAN module is reconstructed to replace ELAN module in original network. Secondly, the InceptionNeXt module is introduced into the neck network to reduce the loss of high-dimensional characteristic information caused by the increase of network depth, and to better carry out multi-scale fusion to improve the detection effect of ships. Finally, the boundary box regression loss function with minimum point distance, namely MPDIoU is used in the detection head to enhance the detection ability in the low-resolution small target scenes. Experimental results on infrared ship dataset show that the precision, recall and mean average precision of the improved algorithm are increased by 3.99, 2.55 and 3.40 percentage points respectively, compared with original YOLOv7 algorithm, and parameters is reduced from 37.23×10⁶ to 31.98×10⁶. In conclusion, the improved algorithm can effectively ameliorate the problems of false detection and missed detection while ensuring the accuracy of infrared ship detection.