# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 3 Results
### Ablation Experiment  
In this section, the ablation test is designed to verify the rationality and effectiveness of each proposed or added module.  
Compared with the original FPN [22], the PSC module and DAF module were added based on it, with both modules being added simultaneously. Experiments are carried out on two datasets: IRSTD-1k and SIRST. The nIoU and F1 scores obtained from the experiment are displayed in Figure 7. Here, \"None\" refers to the original FPN network, \"+PSC\" denotes the addition of the PSC module to the original FPN network, \"+DAF\" indicates the integration of the DAF module into the original FPN network, and \"+PSC + DAF\" represents the inclusion of both modules.  
It can be seen from Figure 7 that both PSC and DAF modules have excellent performance on the two datasets. Compared with the original FPN network, the nIoU and F1 values of the PSC module and DAF module have significantly improved. This suggests that both modules contribute to enhancing detection performance. After adding two modules simultaneously, the values of nIoU and F1 are higher than when adding each module separately. This suggests that the best detection results are achieved only when both modules are used together.  
Secondly, the proposed algorithm is compared with AGPCNet as proposed in [28]. Compared with AGPCNet, the proposed approach incorporates three PSC modules into  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Parameter Setting**} \\  & **Learning Rate** & **Batch Size** & **Epochs** \\ \hline FPN & 0.02 & - & - \\ U-Net & - & - & - \\ TBC-Net & 0.005 & 128 & 130 \\ ALC-Net & 0.1 & 10 & 400 \\ DNA-Net & 0.05 & 16 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for data-driven methods.  
Figure 7: The performance of PSC and DAF modules on both datasets. (**a**) IRSTD-1k, (**b**) SIRST.  
the overall network. These modules are designed to repeatedly process shallow features, enabling a profound fusion of local and global features. This approach is more beneficial for feature representation. In addition, a DAF module is proposed to effectively integrate shallow features and deep features to emphasize important semantic information while downplaying or disregarding unimportant semantic information. The experimental results of the two models on the two datasets are shown in Figure 8. The notation \"+PSC\" denotes adding a single PSC module in the network, specifically in the module where the output is at position \(Z_{3}\), as shown in Figure 1. In contrast, \"+3 PSC\" indicates the inclusion of three PSC modules in the network.  
As depicted in Figure 8, the quantity of PSC modules utilized has a specific influence on the experimental outcomes. The utilization of three PSC modules is superior to using just one, suggesting that employing PSC modules can preserve more intricate low-level information like position and texture without generating excessive redundant data. However, this performance does not imply that adding more PSC modules to the detection model will always result in better outcomes. It needs to be analyzed with respect to specific problems in practical applications. The processing effect of the DAF module proposed in this paper is better than that of AFM, which indicates the effectiveness of the DAF module proposed in this thesis.