# DRUNet: A Method for Infrared Point Target Detection
## 5 Experiments
### Ablation Study
Experiment 2 uses the CBAM module [32], which is also seamlessly integrated into any CNN architecture and is more commonly used than the image attention mechanism, which is more complex than the ECA mechanism and is divided into two parts: spatial attention and channel attention, where the feature map input goes first to the channel attention module and then to the spatial attention module, and then the weights obtained from the attention mechanism are multiplied by the input feature map to perform adaptive feature refinement. The DRUNet with the ECA mechanism is 0.0206 higher than the DRUNet with the CBAM mechanism in terms of accuracy rate. The percentage is 0.1384 lower than that of Experiment 1. This shows that the ECA module is more suitable for the detection of small targets than the CBAM module. Additionally, there is a relatively large difference between the two in terms of inference speed. The model in Experiment 1 has a higher FPS of 80.1783 than the model in Experiment 2. In addition, the model weights in Experiment 1 are slightly smaller than those in Experiment 2 by 0.1 MB. Regarding problem 3, Experiments 3 and 4 were set up for comparison. The model in Experiment 4, like Experiment 3, uses only the residual structure in each block with deep supervision and without using any special attention mechanism. The scatter plot shows that, overall, the model in Experiment 4 without using the residual structure performs the worst, and it has the lowest percentage in most threshold cases. In addition, the difference in accuracy rate between using and not using the participating residual structure is 0.0412, so there is a benefit of using the residual structure in this small target detection task. There is no difference in model size between the two, and the difference in inference speed is also very small. Therefore, the residual structure is indispensable for model performance improvement, especially in the small target detection task. The comparison of Experiments 3 and 4 can answer question 4. The accuracy of the former is 0.0051 higher than that of the latter, which is a relatively small difference, but as \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multirow{2}{*}{**Model**} & **Threshold** & **Threshold** & **Accuracy** & **FPS** & **Model Size** \\  & & **0.5** & **0.75** & **0.95** & **Rate** & **(f/s)** & **(MB)** \\ \hline
1 & DRUNet-wECA & 0.7976 & 0.7633 & 0.403 & 0.8247 & 133 & 10.5 \\ \hline
2 & DRUNet-wCBAM & 0.7771 & 0.7286 & 0.2646 & 0.8041 & 53 & 10.6 \\ \hline
3 & DRUNet-woA & 0.7032 & 0.6696 & 0.3121 & 0.7319 & 182 & 10.5 \\ \hline
4 & DRUNet-woR & 0.6735 & 0.6332 & 0.3328 & 0.6907 & 184 & 10.5 \\ \hline
5 & DRUNet-woD & 0.6901 & 0.6577 & 0.316 & 0.7268 & 183 & 10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table shows the results of the ablation experiment, where the threshold indicates the different OKS thresholds, the corresponding value is the proportion of the number of test samples satisfying the threshold to the total number of test samples, and accuracy rate indicates the ratio of the number of test samples with the distance between the predicted coordinates and the real coordinates to the number of test samples with the radius of the inner tangent circle of the target ground truth less than 1.  
can be seen from the scatter plot, the models with deep supervision are generally better than those without depth. Models with supervision mechanisms perform better. Therefore, the deep supervision mechanism does bring a performance boost to the model with almost no difference in inference speed.