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As a crucial maritime search and rescue method, infrared object detection is critical in influencing the success rate. Research on infrared maritime images is limited, and the problems of smaller object sizes, more substantial noise, and less detailed information still need to be solved. To tackle these problems, we proposed an infrared maritime object detection network with feature enhancement and adjacent fusion. A spatial feature enhancement module and a semantic feature enhancement module are designed to enhance the location information of dim small targets and the deep semantic information, respectively. We designed a feature adjacent fusion network to fully use multiscale feature information. We built a maritime infrared dataset and compared the proposed method with existing advanced traditional and learning methods. The proposed method achieves better detection results.