# DRUNet: A Method for Infrared Point Target Detection
## 1 Introduction  
Infrared detection technology has the characteristics of noncontact and passive detection. Compared with visible image detection, infrared detection technology is less affected by light conditions. Infrared detection technology has the advantages of strong survivability and good portability and can achieve the detection of radar blind areas [1]. Infrared detection and tracking of small targets are widely used in military and maritime fields, such as early warning systems and precision guidance [2]. In recent years, with the development of infrared imaging devices and the popularity of Internet of Things technology, more and more applications have emerged, such as video surveillance, robotic visual navigation, and medical diagnosis [3]. The task of target detection is to be able to identify the category of the target and the location of the target in the given picture or video, which is the basis for tasks such as target tracking. Therefore, research of detection and tracking of infrared small targets has attracted the attention of many scholars. Infrared small targets are characterized by smallness and dimness. Small means that the ratio of pixels occupied by the target to the pixels of the picture is low, and according to the definition of the International Society of Photo-Optical Instrumentation Engineers (SPIE) [4], any target whose size is less than 0.12% of the entire imaging area can be called a small target (i.e., when the image size is 256 \(\times\) 256, the small targets should have no more than 81 pixels, and their target size should be within 9 \(\times\) 9). Dimness refers to the intensity of the target in the infrared wavelengths, specifically reflected in the captured infrared image, and refers to the gray value of the target, which, after imaging the target, may not be the brightest area of the image. To solve the problem of small target detection, scholars have proposed many methods. These methods can generally be divided into two categories: single-frame detection and multiframe detection. Both types of algorithms have their advantages and disadvantages [5]. Single-frame detection algorithms generally have higher miss and false detection rates than multiframe detection algorithms. Multiframe detection algorithms are usually more time-consuming than single-frame detection algorithms, and multiframe detection algorithmsgenerally assume a static background, which makes multiframe detection algorithms unsuitable for many moving camera applications [6]. Single-frame detection methods can be further divided into traditional methods and convolutional neural network-based (CNN-based) detection methods [7]. Traditional single-frame-based image detection algorithms are broadly classified into three types of algorithms based on filtering [8; 9; 10; 11], local contrast [12; 13; 14], and nonlocal self-similarity [15], based on the a priori characteristics of their models. These methods are usually more susceptible to clutter and noise in the background, have insufficient discrimination of scene changes, and are hyperparametrically sensitive, which affects the robustness of detection. Another method is the CNN-based approach; it holds an advantage in the small target detection capability and segmentation integrity. Based on this, we propose a CNN-based infrared small target detection algorithm, which treats small targets as a key point detection task. Experimental validation and performance evaluation were conducted, and the experimental results showed that the algorithm has good performance. The contributions of this paper are as follows:  
1. An effective CNN-based infrared small target detection algorithm is proposed, containing a feature extraction module and a prediction head module, where the feature extraction part can be used for other infrared small target vision tasks. 2.