# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
###### Abstract  
Technologies for the detection of dim and small targets in infrared images play an increasingly important role in various applications, including military early warning, precise guidance, military reconnaissance, environmental monitoring, and aerospace applications. This paper proposes a new approach for the detection of infrared multi-scale small targets based on a feature pyramid network. Three pyramid segmentation-connection modules are incorporated into the proposed pyramid network to capture both local and global context information across various layers. Furthermore, a dual attention fusion module is proposed to fuse the feature maps containing context information and the deep features that have been upsampled twice through the attention mechanism of the dual attention fusion module to highlight important semantic information. Experimental results on two benchmark datasets show that the proposed method can generate results with good accuracy on both datasets and outperforms several other state-of-the-art methods for small-target detection in terms of accuracy and robustness. object detection; deep learning; feature pyramid network; attention mechanism fusion +
Footnote †: journal: Applied Sciences  
18 May 2020 Revised: 16 June 2020 Revised: 18 June 2020 Revised: 18 June 2020 Revised: 27 June 2020 Revised: 18 May 2020 Revised: 18 May 2020 Revised: 16 June 2020 Revised: 18 June 2020 Revised: 27 June 2020 Revised: 27 June 2020 Revised: 2  
learning-based methods can be divided into two types in the early stages. One type of method utilizes candidate boxes to perform detections in two stages [17], and the other type of method detects infrared small targets in one stage based on regression [18; 19]. However, due to the scarcity of infrared dim small-target datasets, the challenges in training network models, and the degradation of infrared dim small-target features as the number of network layers increases, further improvements still need to be achieved in the performance of infrared dim small-target detection. In the realm of target detection, infrared small-target detection can also be accomplished as an object segmentation task. Applying RGB image segmentation based on deep convolutional neural networks to the segmentation of small targets in infrared images remains a challenge that requires further exploration [20]. Recognizing objects of various sizes is a fundamental challenge in object detection. Pyramid networks have been a fundamental component of multi-scale object detection. However, the pyramid network involves a significant amount of computation, which could potentially slow down the entire detection process. Therefore, in order to enhance detection speed, many methods opt to avoid using pyramid networks and instead rely solely on high-level features for prediction. High-level features contain rich semantic information, but accurately capturing the position of the object is challenging due to the low resolution [21]. On the contrary, although low-level features contain less semantic information, they offer high resolution and rich details, enabling accurate representation of object locations. Therefore, the fusion of low-level and high-level features can achieve accurate recognition and localization in object detection systems. This fusion can combine the rich semantic information in high-level features with the accurate location information in low-level features to achieve more precise target detection and localization. [PERSON] et al. [22] proposed the feature pyramid network (FPN) in 2017, which has significantly improved the performance of small-object detection without increasing the original computational load. [PERSON] et al. [23] proposed the U-Net architecture in 2015. Although the U-Net architecture is not specifically designed for detecting infrared small targets, it has demonstrated excellent performance in infrared small-target detection. [PERSON] et al.