A low-altitude sea surface infrared object detection method based on unsupervised domain adaptation is proposed. First, the source domain images are translated into target domain images by image translation network, and the labels are shared. Second, the gradient reversal layer is used in YOLOv5s object detection network to optimize the inter-domain adaptability of feature extraction. In addition, the maximum mean discrepancy loss is used to further narrow the feature distribution of different infrared detector images extracted from the network. Finally, AdamW asynchronous update optimization algorithm is adopted to further improve the training stability and detection accuracy. The proposed method is tested on low-altitude sea surface infrared ships and unmanned aerial vehicles collected by different infrared detectors. Experimental results show that compared with the traditional supervised learning method, the proposed method effectively reduces the cost of manual labeling, and detection accuracy of source domain and target domain are improved by 6.56 and 2.62 percentage points respectively, which effectively improves the generalization ability of the object detection model between different infrared detectors.