基于多尺度特征融合的红外小目标检测方法

红外小目标检测因其探测距离远、抗干扰能力强等特点，在空中目标探测与跟踪系统中得到了广泛的应用。针对目前红外小目标检测算法在复杂背景下检测准确率低、虚警率高等缺点。提出了一种基于多尺度特征融合的端到端红外小目标检测模型（multi-scale feature fusion single shot multibox detecto，MFSSD）。考虑到红外小目标的特点，通过细化和融合特征图的方法提出了一种特征融合模块，通过SP模块提高特征图不同通道的相关性，3种不同序列红外图像的实验结果表明，该算法在红外小目标检测中的平均检测精度高达87.8%。与传统的多尺度目标检测算法相比，准确率和召回率都有显著提高。

Infrared Small Target Detection Method Based on Multi-Scale Feature Fusion

Infrared small target detection is widely used in aerial target detection and tracking systems owing to its long detection range and strong anti-jamming ability. Aiming at to overcome the shortcomings of the current infrared small target detection algorithm, such as a low precision rate and high false alarm rate when dealing with complex backgrounds, we propose an end-to-end infrared small target detection model (called MFSSD) based on multi-scale feature fusion. Considering the traits of the targets, we propose a feature fusion module using a refinement and fusion feature map method and improve the correlation of different channels through the SP module. The experimental results of three different sequences of infrared image detection show that the average detection accuracy of the MFSSD algorithm for infrared small target detection was as high as 87.8%. Compared with those of the traditional multi-scale target detection algorithm, both the precision rate and recall rate have been significantly improved.