# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 1 Introduction
Typical methods include the three-dimensional matching filter [12], the optical flow method [13], the Kalman filter [14], and the particle filter [15]. [PERSON] et al. [16] proposed a coarse-to-fine three-dimensional directional search filter based on three-dimensional matching filtering to reduce computational demands. However, the computational complexity remains high, leading to significant detection time costs. [PERSON] et al. [17] developed a 3D Hough transform (3D-HT) algorithm that uses two sets of coordinates and their errors from different times to construct a 3D pipeline, but it struggles to meet real-time performance requirements when the target's speed changes rapidly. Traditional algorithms rely heavily on manually designed features, which demand extensive debugging and struggle to accommodate the diverse detection needs across various scenarios. In recent years, the rapid progress of deep learning has revolutionized image processing, particularly in optical target detection and recognition. The integration of deep learning techniques into classifier and algorithm design continues to be a major research focus in this field [18; 19]. Deep learning-based object detection methods are generally categorized into two types. The first type uses convolutional layers to extract target features and generate numerous candidate regions where the target may exist. The target detection network then outputs information such as target position and category within regions, as seen in methods like region convolutional neural networks (R-CNN) [20], fast RCNN [21], and others. The second type directly predicts the location, category, and other relevant information for each small region of the input image, such as in the single shot multibox detector (SSD) [22]. Deep learning-based DBT methods like those of [PERSON] et al. have modified the structure of the channel attention module (CAM) and spatial attention module (SAM) to achieve multi-scale feature fusion [23]. However, feature learning for dim and small targets with heavy background clutter remains challenging due to the lack of clear texture and shape features in infrared dim and small targets. [PERSON] et al. [24] proposed an enhanced convolutional neural network (CNN) for infrared images based on single-frame target detection and recognition principles, but it also introduced more noise. [PERSON] et al. [25]incorporated the adaptive fusion attention module (AFAM) into the you only look once (YOLO)v5 model to weight features and improve detection accuracy, but its precision remains insufficient in complex scenes. Deep learning-based TBD methods like those of [PERSON] et al. (2016) extract short-term and space-dimensional information from 15 consecutive infrared image sequences using a 3D convolutional kernel, combined with a convolutional long short-term memory network (LSTM) to capture long-term spatio-temporal information. However, issues with missed detections and false alarms persist, and the method does not meet real-time requirements. [PERSON] (2017) combined the LSTM prediction network with the duality of convolutional neural networks (Du-CNN) classification network to create the cross duality of neural network (CR-DUNN), which classifies targets from clutter by fully utilizing information from both the present and future moments. [PERSON] et al. (2018) improved detection accuracy by around 5% through the bidirectional fusion of deep and shallow features, along with skip connections to capture richer contextual information. However, their network model is large and contains too many parameters, resulting in high computational resource consumption. These methods are still falling short in accurately detecting dim and small infrared targets in complex scenes, with continued occurrences of missed and false detections. This paper introduces an algorithm for infrared dim and small target detection based on local-global feature fusion to address these challenges.