针对仅配备CPU的红外成像系统,本文提出了一种基于中心点的实时目标检测方法.遵循轻量化的设计原则,首先引入了低计算成本的特征提取网络,并在此基础上设计了相应的特征融合模块以充分利用不同阶段提取的空间和上下文信息.同时为了进一步提高网络的表征能力,提出了一个背景抑制模块以完成对前景区域的特征增强,并最终通过轻量检测网络实现对目标中心点及其相应属性的预测.在红外空中目标数据集上的实验表明,本文所提方法能够在CPU上以21.69 ms每帧的速度达到90.24％的检测精度.与经典的Tiny-YOLOv3相比,在计算量和参数量仅为前者21％和11％的前提下,检测精度提高了10.94％,并且检测速度提高了10.02 ms,证明了方法在实时红外系统中的巨大应用潜力.“,”A real-time target detection method based on center points is proposed for infrared imaging systems equipped with CPUs. Following the lightweight design principles,a backbone with low computational cost is first introduced for feature extraction. Correspondingly,an efficient feature fusion module is designed to exploit spa? tial and contextual information extracted from multi-stages. In addition,an auxiliary background suppression module is proposed to predict foreground regions to enhance the feature representation. Finally,a simple detec? tion head predicts the target center point and its associated properties. Evaluations on the infrared aerial target da? taset show that our proposed method achieves 90. 24％mAP at a speed of 21. 69 ms per frame on the CPU. It sur? passes the state-of-the-art Tiny-YOLOv3 by 10. 16％mAP with only 21％FLOPs and 11％parameters while also runs 10. 02 ms faster. The results demonstrate its great potential for real-time infrared applications.