## 4 Experiment and Analysis
### Comparison of Diverse Models on FLIR Dataset  
To further verify the efficacy of the proposed method, several different single-stage target detection algorithms were selected by utilizing the uniform equipment and infrared dataset and setting the uniform experimental arguments. The performance of the infrared target detection algorithm in a complex environment was compared using various detection and evaluation indices. Figure 8 includes the visual effect presentation of the original label, YOLOv5, YOLOv7-tiny, and the method proposed in this study. Obviously, our proposed model has excellent performance in detecting easily occluded infrared objects in intricate conditions, which improves the model's resistance to noise and interference. For example, on account of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**ISW** & **TAH** & **C2f** & **Precision** & **Recall** & **F1** & **mAP** \\ \hline  & & & 85.5 & 71.3 & 78 & 80.9 \\ ✓ & & & 86.8 & 72.8 & 79 & 81.9 \\  & ✓ & & 86.2 & 73.3 & 79 & 82.0 \\  & & ✓ & 85.7 & 72.1 & 78 & 81.4 \\ ✓ & ✓ & & 86.4 & 72.6 & 79 & 81.8 \\ ✓ & & ✓ & 86.5 & 73.2 & 79 & 82.3 \\  & ✓ & ✓ & 85.6 & 74.6 & 80 & 82.8 \\ ✓ & ✓ & ✓ & 86.8 & 75.7 & 81 & 84.0 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of network performance under various combinations modules, where ✓ stands for the module currently in use. Figure 7: The mAP performance curves for incremental ordering of modules. The curve shows that the combination of Benchmark+TAH+C2f+ISW has the best performance. the low contrast of infrared images in a real environment, the distinction between the target and the backdrop becomes blurred, increasing the difficulty of detection. During the detection using YOLOv5 and YOLOv7-tiny, there was a situation of missing detection, and no distant infrared small target was identified. However, the green circle marked in Figure 8 indicates that the network proposed in this paper could comprehensively identify all objects and avoid missing detection, indicating that the improved model has certain universality and adaptability, not limited to specific types of targets, provides a reliable solution for application in actual scenes, and can better maintain detection performance even in the case of intricate conditions or occlusion. The data in Table 4 clearly reveal that compared with the most advanced target-detecting models at present, the method in this study provides leading improvements in several indicators, with mAP, precision, and F1 scores reaching 84%, 86.8%, and 81%, respectively. Specifically, the proposed algorithm achieves mAP and F1 that are 16.1% and 13% higher than those of the SSD algorithm, while the model size is only one-fortieth, demonstrating improved detection results. In addition, our method mAP outperforms the latest lightweight YOLOv7-tiny algorithm by 5.2% in terms of precision. The above analysis illustrates that the method in this study has a strong feature expression ability, which is more in line with real needs and is more stable and reliable for practical use. Figure 9 concisely and intuitively illustrates the overall trend in the model across diverse metrics to better understand the holistic performance characteristics of the model. Figure 8: Visual effects of diverse infrared detection models based on FLIR dataset. (**a**) Ground truth of the original label; (**b**) YOLOv5n; (**c**) YOLOv7-tiny; (**d**) ours. Within the realm of infrared detection, on account of the open source and data diversity of the FLIR dataset, many scholars have explored of infrared object detection with a basis in this public resource in recent years. Therefore, aiming at the three categories of AP and mAP indicators, this study selected the most popular and excellent infrared image detection methods to carry out a succession of comparative analyses. In addition to the YOLO series, the comparison method also included MMTOD-CG, TermalDet, CMPD, and other multimode detectors based upon the complementary information fusion of visible and infrared images.