针对资源受限的红外成像系统准确、实时检测目标的需求，提出了一种轻量型的红外图像目标检测算法GPNet。采用GhostNet优化特征提取网络，使用改进的PANet进行特征融合，利用深度可分离卷积替换特定位置的普通3×3卷积，可以更好地提取多尺度特征并减少参数量。公共数据集上的实验表明，本文算法与YOLOv4、YOLOv5-m相比，参数量分别降低了81%和42%；与YOLOX-m相比，平均精度均值提高了2.5%，参数量降低了51%；参数量为12.3 M，检测时间为14 ms，实现了检测准确性和参数量的平衡。

A lightweight infrared image target detection algorithm GPNet is proposed to address the need for accurate and real-time target detection in resource-constrained infrared imaging systems. The feature extraction network is optimized using GhostNet, feature fusion is performed using an improved PANet, and a depth-separable convolution is used to replace the ordinary 3×3 convolution at specific locations to better extract multi-scale features and reduce the number of parameters. Experiments on public datasets show that the algorithm in this paper reduces the number of parameters by 81% and 42% compared with YOLOv4 and YOLOv5-m, respectively; the average mean accuracy is improved by 2.5% and the number of parameters is reduced by 51% compared with YOLOX-m; the number of parameters is 12.3 M and the detection time is 14 ms, which achieves a balance between detection accuracy and number of parameters.