针对红外舰船小目标图像复杂背景弱信号，虚警率较高且难以被精确检测的问题，提出了一种恒虚警率(Constant False-Alarm Rate，CFAR)-全连接条件随机场(Dense Conditional Random Fields，DCRF)舰船目标检测算法.该算法针对小目标与虚警信号变化特征相似但结构特征不同的特点,利用CRF的多维上下文(空间、辐射)表达的优势，实现虚警特征抑制，并引入CFAR对模型进行改进，提高了DCRF对于弱信号目标的检出能力,实现舰船小目标的精确检测与分割.实验结果表明，该算法能够充分利用海域的全局上下文信息，能够在保持较高检出率同时，有效降低虚警率，实现单帧端到端的小目标检测.

This paper focuses on the problem of low detection accuracy and low extraction accuracy of the traditional small target detection and ship detection methods. An improved target detection algorithm based on constant false-alarm rate(CFAR)-dense conditional fandom fields (DCRF)is proposed. The algorithm is based on the characteristics of small target and false alarm signal changes with different structural features. It uses the advantages of conditional random fields (CRF) in multi-dimensional context (space, radiation) to achieve false alarm feature suppression, and introduces CFAR to improve the model and improve DCRF. Based on this model, experiments were performed under different conditions. The analysis results show that the algorithm can make full use of the global context information of the sea area, and can reduce the false alarm rate while maintaining a high detection rate.