随着图像中显著目标检测准确率的提高，人们关注的重点逐渐成为如何提升小目标的检测精度。然而，现有的目标检测方法主要研究以可见光图像作为输入的通用目标检测问题，小目标检测领域的大部分方法针对可见光图像，面向红外图像的小目标检测研究较少。红外小目标不含颜色信息，与常规目标尺度差别大，更加依赖上下文信息。针对这些问题，提出一种基于YOLOv5的红外小目标检测模型。在标准的YOLOv5模型基础上，为了有效结合目标周围的局部信息和整体特征中的全局信息，同时适应红外小目标的细微形态变化，提出了动态上下文信息提取模块（Dynamic Contextual Information Extraction Module）；引入通道-细节注意力模块（Channel-Detail Attention Module）汇聚红外小目标的通道信息和细节信息，提高回归精度；考虑到网络卷积过程中细节特征丢失的问题，在保证模型特征尺度相对应的情况下，上采样新的特征尺度来与浅层特征融合，捕捉更多红外小目标细节信息，避免特征混叠。为了证明方法的有效性，在公开的红外数据集ITTD、IRSTD-1k和NUAA-SIRST上进行验证。实验结果表明，在ITTD数据集中所提方法超过对比方法的mAP值5.1%。对比YOLOv5s基准模型，mAP值提高了3.7%，在IRSTD-1k和NUAA-SIRST数据集中也展示出了良好的检测效果，并对自身模型进行了消融实验。本文所提出的红外小目标检测模型对复杂场景下的红外小目标有着很好的鲁棒性，有效地提高了小目标检测的精度，降低了小目标的漏检率。

As the accuracy of salient object detection has been improved, the focus has gradually shifted to how to improve the accuracy of small object detection. However, existing object detection methods mainly study the general object detection problem using visible images as input, and the most of small object detection methods are designed for visible images, leaving the small object detection on infrared images underexplored. Compared with standard scale objects, infrared small objects lack color information, which makes them more dependent on contextual information. To solve this problem, an infrared small object detection model is proposed based on the standard YOLOv5 model. The local information around small objects is effectively combined with global information by the newly introduced Dynamic Contextual Information Extraction Module, which adapts to the subtle morphological changes of infrared small objects dynamically. The Channel-Detail Attention Module is designed to aggregate the channel and detail information of the infrared small objects to improve the accuracy of the regression. To use the shallow features to enhance the detail information of infrared small objects, the feature with new scales are upsampled and fused with the shallow features, which captures more detailed information about infrared small objects and avoids feature blending. To demonstrate the effectiveness of the proposed method, extensive experiments are conducted on the public infrared datasets, including ITTD, IRSTD-1k, and NUAA-SIRST. The experimental results show that the proposed method outperforms the compared methods in terms of mAP by 5.1% on the ITTD dataset, and the mAP is also improved by 3.7% when compared to the baseline method (i.e., YOLOv5). Results on the IRSTD-1k and NUAA-SIRST datasets also demonstrate the effectiveness of our design. An ablation study is performed to verify the effectiveness of different modules. The proposed infrared small object detection model is robust to the infrared small objects in complex backgrounds, which improves the accuracy and reduces the false alarm rate of infrared small object detection effectively.