Aiming at the problems of low detection accuracy and lack of real-time performance of existing infrared ship detection algorithms, an infrared ship detection algorithm based on improved YOLOv8 is proposed. Firstly, the Multiscale Coordinate Attention (MCA) mechanism designed in this paper is introduced into the backbone network of YOLOv8 to enhance the capability of multi-scale feature extraction.Secondly, the YOLOv8 detection head is designed with shared parameters and re-parameterization, so as to improve the detection efficiency of the detection head.Then, the neck network of YOLOv8 is improved by using BiFPN structure, and the feature expression capability of the network is enhanced by bidirectional information flow and learnable weights.Finally, Faster Block is used to improve the C2f module of YOLOv8, which can maintain the accuracy while reducing the quantity of parameters and improve the detection speed of the model.The algorithm is tested on the infrared ship data set, and the mAP value reaches 93.1%, which is 2.5 percentage points higher than that of the original model, and the quantity of parameters is 32.6% lower than the original model.The experimental results show that the improved algorithm is much better than the original algorithm, which proves the effectiveness of the improved algorithm.