# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments  
### Data and Experiment Setup  
The complexity of infrared dim and small target detection is measured by the local signal-to-noise ratio (LSNR). The smaller the LSNR, the smaller the intensity difference between the target and the background, making it more difficult to detect the target. The local signal-to-noise ratio (LSNR) is calculated as follows:  
\[LSNR=10\log_{10}(E_{r}-E_{B})/\delta_{B} \tag{3}\]  
where \(E_{r}\) is the mean intensity value of the target area, \(E_{B}\) is the mean intensity value of the background region surrounding the target, and \(\delta_{B}\) is the intensity standard deviation of this background region, which is three times the size of the target. Figure 4: The structure of feature fusion. Figure 3: Dual-path feature fusion network. To evaluate the performance and robustness of the proposed method, infrared images from four different scenes were used. The dataset includes a total of 12,500 images, including aerial surveillance, complex cloud background, and simple background, with each image containing one infrared dim and small target. One of these datasets was taken with a drone, five datasets were generated through simulation based on real infrared backgrounds and targets with varying LSNR, and one additional dataset was taken on the ground. Note that all simulated data were generated using a similar method [31] in the scene. Data1 and data2 depict sky scenes characterized by pronounced background edge clutter, with data1 captured by drones, while data3 and data4 show complex cloud scenes, where the target can easily blend into the clouds. Data5 and data6 consist of simpler smoother cloud scenes. Lastly, data7 include real images captured at night by a camera on the ground. These images are representative of the infrared dim and small targets in most sky backgrounds. All data were labeled by experts in the field of infrared detection and machine vision using rectangular box annotations. To ensure labeling quality, each infrared image was reviewed by three experts, with one assigned as the responsible expert. After the initial annotation by one expert, a second expert verifies it, and the responsible expert gives final confirmation. Details of the dataset are shown in Table 1, and Figure 5 exhibits representative images under varying backgrounds and signal-to-noise ratio conditions. The targets within the images are marked with infrared rectangles, and all images are of the same size. To ensure accurate evaluation, we partitioned the dataset into training, validation, and testing sets using a 7:2:1 ratio, respectively. The proposed model was implemented in Python 3.8, running on Windows 11, with CUDA 11.4 using the PyTorch 1.11.0 backend and cuDNN 8.2.0. The system was set up on a workstation with an Intel Core [EMAIL_ADDRESS] GHz processor with 64 GB of RAM and an NVIDIA GTX 3080 Ti GPU with 12 GB of memory. The entire training process used a batch size of 64, and it ended after 300 epochs. We utilized mAP\({}_{50}\) and mAP\({}_{50.95}\) as comparative metrics for evaluating the model's performance. Mean average precision (mAP) reflects the average accuracy across the dataset, with mAP\({}_{50}\) serving as an indicator of the algorithm's overall classification ability across different target types. mAP\({}_{50.95}\) measures the average detection accuracy across all 10 intersection over union (IoU) thresholds, ranging from 0.5 to 0.95, with a step size of 0.05. Generally, the higher the IoU threshold, the higher the demand on the model's regression ability, resulting in detection outcomes that align more closely with the actual target. In addition, we introduce the true positive rate (P\({}_{\text{d}}\)) and false positive rate (F\({}_{\text{a}}\)) [32] to further evaluate detection performance.