# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 1 Introduction  
In the field of infrared target detection, the imaging system captures images based on the thermal radiation signals emitted by the target within the 8-12 \(\upmu\)m wavelength range. Since infrared imaging depends on the target's temperature and material properties, it demonstrates strong resistance to interference and can be used effectively in a wide range of conditions. This makes it indispensable for precision guidance, surveillance, and tracking [1; 2; 3]. According to the definition of dim and small targets provided by the International Society for Optical Engineering (SPIE), a target is considered dim and small if it occupies no more than 0.15% of the total image, has a size of fewer than \(9\times 9\) pixels (typically ranging from \(2\times 2\) to \(10\times 10\) pixels), and has a local signal-to-noise ratio (LSNR) or no greater than 5 dB [4; 5]. Compared to general target detection, infrared dim and small target detection has distinct characteristics. First, the target is small and has an inconspicuous structure in long-distance detection and early warning guidance under infrared backgrounds, so it lacks obvious shape or texture features that are easily extracted. Second, the target has a low LSNR, making it more susceptible to being obscured by complex backgrounds compared to visible light images. As a result, frequent false detections and alarms occur, significantly reducing the accuracy of target detection in intricate environments. The fundamental concept behind infrared dim and small target detection algorithms involves background suppression and target energy enhancement. Currently, researchmethods in this field can be divided into two categories: detect before tracking (DBT) and tracking before detect (TBD). DBT, also known as the detection method based on measured data from the current frame, enhances the target and suppresses background clutter in the image containing the target, then sets a threshold to segment the target from the processed image. Clutter refers to background information or interference unrelated to the target, including areas of the background and noise that are similar to the target. Traditional single-frame detection methods include wavelet transform [6], top-hat [7] and two-dimensional least mean square filtering [8]. [PERSON] et al. [9] proposed a denoising threshold function suitable for rotating multi-element infrared detection systems, which better retains target features, though this method is not robust against varying background clutters. [PERSON] et al. [10] employed the Gaussian Laplace filter to obtain target response maps with both large and small pixel values, extracting targets through adaptive threshold segmentation. While this approach achieved good detection results, the algorithm's high complexity limits its use for real-time detection. [PERSON] et al. [11] utilize a continuous adaptive structure combined with quantum genetics to optimize the overall algorithm, but this approach does not align well with the geometric characteristics of image sequences, and it may also distort the shape of the target due to structural elements that are either too large or too small. TBD, or tracking before detect, refers to a detection method based on multi-frame images, utilizing spatio-temporal information from continuous frames, such as motion paths, brightness, and scale changes to detect the targets with consistent trajectory or velocity priors. It firstly tracks a target by considering hypothetical potential paths in the multi-frame data, continuously accumulates the target's energy, and finally screens candidate targets using a discriminant operator. Since TBD sets no threshold or merely a low threshold for single-frame data, it retains as much information about dim and small targets as is possible, helping to avoid the target loss problem faced by the DBT method. By accumulating insights from multiple frames, TBD aims to accurately pinpoint the target's position within the image.