# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments
### Experiments and Analysis
#### 3.2.1 Ablation Study  
To comprehensively validate the sensitivity of the dual-path feature fusion extraction network to variation in infrared dim and small targets, as well as its detection efficacy across diverse scenes, we conducted experiments on datasets with different local signal-to-noise ratios in various scenes. Both the YOLOv7 model (baseline model) and the proposed model were employed. The outcomes of these experiments are shown in Table 2.  
As is evident from the experimental results shown in Table 2, under various complex backgrounds, the algorithm employing local-global dual-path feature fusion demonstrates notable improvements in both mAP50 and mAP50;95 compared to the original YOLOv7. Specifically, mAP50 exhibits an average increase of over 2%, while mAP50;95 experiences an average increase of more than 3.6%. These findings underscore that the utilization of local magnification enhances the network's ability to extract both spatial and semantic feature information from the target. In the same scene, the experiment conducted by varying the local signal-to-noise ratio of the target reveals that even under conditions characterized by an extremely low local signal-to-noise ratio, the performance of the dual-path feature fusion algorithm shows an increase of more than 2.4% compared to the original YOLOv7 network, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Scene** & **mAP50(Ours/YOLOV7)** & **mAP50;95(Ours/YOLOV7)** \\ \hline
1 & Bright, thin cloud & **0.8942**/0.8463 & **0.5758**/0.559 \\
2 & & **0.9964**/0.97 & **0.8064**/0.7535 \\ \hline
3 & Heavy cloud cover & **0.7793**/0.7522 & **0.5537**/0.4984 \\
4 & & **0.9917**/0.9567 & **0.8408**/0.7924 \\ \hline
5 & Dim, thin cloud & **0.75**/0.7083 & **0.5557**/0.4881 \\
6 & & **0.9779**/0.9549 & **0.8138**/0.7884 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results under different scenario data. Figure 5: A frame of different data sequence images. Dim and small targets are marked with a red rectangle, and all images are the same size. despite a slight decline in detection accuracy. This underscores the efficacy of the dual-path fusion strategy in effectively enhancing the spatial and semantic feature information of dim and small targets within complex scenes, while simultaneously reducing the interference of noise. To verify the effectiveness of the network module proposed in this chapter for infrared dim and small target detection, we compared the mAP\({}_{50}\) and mAP\({}_{50:95}\) results from ablation experiments conducted on data7. The experimental results are shown in Table 3.  
As shown in Table 3, scheme one refers to the original YOLOv7 network. Scheme two enhances the network by incorporating a local magnification branch for feature extraction. Scheme three, building on scheme two, performs feature fusion at the end of each feature extraction stage, integrating features from the main path. Notably, all schemes utilize the same pre-trained weights. As observed from the detection results of data7 in Table 3, the inclusion of a local magnification branch for infrared small targets has a certain inhibitory effect on the background. When the main network incorporates a local branch and performs feature fusion, both mAP\({}_{50}\) and mAP\({}_{50:95}\) improve compared to the YOLOv7 model. This improvement is due to the fact that during feature extraction, the feature information of dim and small targets tends to dilute or even disappear as the network deepens. The local branch, however, has the ability to enhance the feature information of the dim and small targets during this process. Additionally, the multi-scale feature fusion helps retain this information, significantly boosting detection capabilities. The following figure presents several detection result images of the YOLOv7 model and our proposed algorithm on the dataset. The post-detection images are displayed in Figure 6, where it is clear that our model demonstrates superior performance, as evidenced by a higher confidence score.