为了提高红外舰船图象检测的实时性，提出了一种基于多分辨率注意机制的红外舰船图象检测方法，该方法是利用注意机制来降低待处理数据量，并将注意过程分为“预注意”和“注意”两个阶段，同时采用非线性采样模型，在降低预注意分辨率的同时，使该方法能适应目标大小变化 ;检测时，将红外舰船图象中舰船发动机或烟囱所在的热区域作为“预注意”的特征，先将注意引导到感兴趣区域上，再在感兴趣区域内检测吃水线特征.实验证明，该方法能有效地提高红外舰船检测的效率，并对目标大小变化有一定的适应能力.

In order to improve the ability of real time computation, an attention-based IR ship detection is proposed to reduce the data to be processed and enhance the processing efficiency. The attention is divided into "pre-attention" and "attention". A nonlinear sampling model is adopted to reduce the resolution in pre-attention while keeping adaptive to size variance of the target; the hot region which always refers to the engine or chimney of a ship is adopted as the guidance of attention to the areas of interest(AOI), then the waterline, which is taken as the less salient feature of infrared ship target, is detected in the AOI. If the waterline feature exists in an AOI, it means a target is detected; otherwise, the AOI is taken as false alarm. To test the performance of the approach proposed, an algorithm is designed and realized both on PC and on a multi-DSPs system. Experiments demonstrate that the approach proposed can enhance the detection efficiency, and it is adaptive to the size of target.