# DRUNet: A Method for Infrared Point Target Detection
## 4 Training Method  
### Dataset  
There are few publicly available datasets for single-frame IR small target detection, and most of them are collected and produced by researchers, so unlike computer vision tasks based on visible image datasets, IR small target detection and tracking have long suffered from data scarcity. However, some higher-quality IR datasets have emerged in recent years, such as [PERSON] [17] et al. The proposed SIRST (single-frame infrared small target) dataset is the first open single-frame dataset with real IR images for both background and target, and they identify real IR targets rather than pixel level by collecting IR sequence pulse noise, selecting only one representative image from each sequence. In addition, these images are annotated in five different forms to support detection tasks in different formulations of modeling. SIRST is not only the first publicly available but also the largest compared with other private datasets. The SIRST dataset contains 427 images, including 480 instances, roughly divided into 50% training, 20% validation, and 30% testing. [PERSON] et al. [30] established another large open SIRST dataset. Compared with the SIRST dataset presented above, the dataset they constructed has a larger amount of data, but it contains some synthetic images. Additionally, the specific synthesis method is to superimpose the small target objects synthesized by the two-dimensional Gaussian function on the obtained background to form a new image, but many of the targets in the dataset do not meet the definition of small targets. The small targets synthesized by the two-bit Gaussian function do not fully reflect the characteristics of the real small targets. To ensure that the network can better have the ability to discriminate small infrared targets, this paper selects the SIRST dataset proposed by [PERSON] et al. while using some real image enhancement datasets from the infrared sequence dataset. The ground/air background infrared image small aircraft target detection tracking dataset is a very suitable data source for the expansion of the IR small target detection dataset, which acquires scenes covering the sky, ground, and other backgrounds, as well as a variety of scenes, a total of 22 segments of data, 30 traces, 16,177 frames of images, and 16,944 targets. Each target corresponds to a labeled location, and each segment corresponds to each target corresponding to a labeled location, and each data segment corresponds to a labeled file. The main facilities and equipment used for data acquisition are the tower and the two-axis electric control turntable. The data acquisition test was carried out by using a tower with a two-axis electronically controlled turntable and an infrared sensor. The use of the tower and the two-axis turntable provided a guarantee for acquiring dynamic infrared sequence images of targets in different environmental backgrounds and different observation perspectives. The characteristics of the targets in this dataset are: the target scale varies greatly due to the great difference in the shooting distance, and the UAV is shown as an extended target in the IR images at closer distances and as a point target at longer distances; the thermal radiation characteristics of the targets in the IR images make them relatively homogeneous in texture; that is, under sunlit conditions in clear skies, the UAV body is shown as a highly radiated bright area throughout. Under no sunlit conditions, only the UAV nose engine position is shown as a bright area [31]. It can be seen that in this dataset, the target with certain changes, can be used to obtain a rich infrared small target training set. The dataset used for the experiments in this paper is composed of data-cleaned SIRST and images and extracted labels obtained by frame extraction from the infrared image weak aircraft target detection and tracking dataset in ground/air background, with a total of 1034 images, 840 images used in the training set and 194 images used in the test set.