## 4 Experiments
### Experimental Results and Analysis  
#### 4.3.1 Target Detection Network Training and Testing  
1. Evaluation and analysis of the effectiveness of the improved YOLOv4 network model;  
In this experiment, the added scSE module mentioned above is trained on the thermal imaging dataset established in this paper. The model performance is evaluated on the test set. The original YOLOv4 model is used as a baseline for performance comparison to study the performance changes brought by the three variants of SE embedded in different network positions. The experimental results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]\%** \\ \hline YOLOv4(Baseline) & 97.97 & 95.09 \\ scSE-YOLOv4 & 95.67 & 92.63 \\ scSE-YOLOv4 & 89.32 & 81.78 \\ cSE-YOLOv4 & 98.31 & 98.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance evaluation of the three variants embedded in different positions of the network on the test set.  
Figure 9: Self-collected dataset of thermal imaging abnormal behavior in indoor scenes. (**a**) walk (**b**) sit down (**c**) bend over (**d**) crouch down (**e**) fall down.  
The experimental parameters are set as follows: the input resolution of the network during training is 418 \(\times\) 418, the network model is optimized by the Adam algorithm, the initial learning rate is set to 0.001, and a total of 100 epochs are trained. Table 1 shows the results of the model performance evaluation metrics obtained from the trained model tested on the test set, where the IOU threshold is set to 0.6 common to model evaluation, that is, [EMAIL_ADDRESS]. Similarly, [EMAIL_ADDRESS]/% represents the detection accuracy when the IOU threshold is 0.7.  
It can be seen that embedding the cSE attention module after the \"add\" and \"concat\" feature fusion layer of the backbone can effectively improve the algorithm's performance in detecting small thermal imaging targets, with [EMAIL_ADDRESS] and [EMAIL_ADDRESS] values improving by 0.34% and 3.22%, respectively, compared to YOLOv4. However, embedding the scSE module and sSE module into the network's backbone results in different degrees of degradation. We attribute this to the fact that the cSE module enhances the learning of essential channel features in the thermal imaging images, but the addition of the sSE module compresses the channel features of the thermal imaging feature maps and disrupts the spatial representation of the features.  
Figure 10 shows the comparison plots of the results of 4 consecutive frames obtained using YOLOv4 and the improved cSE-YOLOv4 algorithm in the detection of the self-collected thermal imaging dataset. It can be seen that the target frames predicted by the original network in frames 3 and 4 are not appropriate. In contrast, the improved network can give more accurate target frames, providing a more accurate human target frame for the next step of feature extraction of the behavioral parameter field.  
Combining Table 2 to compare the detection accuracy of different algorithms on IR datasets, the improved cSE-YOLOv4 model in this paper shows the highest detection performance and proves its effectiveness on small target detection.