# DRUNet: A Method for Infrared Point Target Detection
## 5 Experiments
### Contrast Experiment  
We selected three feature extraction networks for comparison, and their output feature maps are all the same size as the original input maps. The prediction head uses the same parameters, and the details of the training process are the same as described in the training setup. The results are shown in Figure 9.  
As shown in Table 2, Experiment 1 is our proposed model. The backbone of Experiment 2 is UNET++, and the number of channels varies from shallow to deep 16, 32, 64, 128, and 256, which is consistent with the number of channels per layer variation of DRUNet. The backbone of Experiment 3 is UNet [33], and the biggest difference between UNET and UNet++ is that it only uses long links. Additionally, the overall network shape is simply Experiment 4 uses FCN as the backbone, and FCN [34] is an end-to-end, pixel-to-pixel fully convolutional network for semantic segmentation of images. The overall network structure is divided into two parts: the full convolutional part and the up-sampling part. FCN-32s use the convolutional part of vgg16 as the backbone and change the last three fully connected layers of vgg16 to convolutional layers as well. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiments** & **Model** & **Threshold** & **Threshold** & **Accuracy Rate** & **FPS** & **Model Size** \\  & **0.5** & **0.75** & **0.95** & **Accuracy Rate** & **(f/s)** & **(MB)** \\ \hline
1 & DRUNet-wECA & 0.7976 & 0.7633 & 0.403 & 0.8247 & 133 & 10.5 \\ \hline
2 & UNet++ & 0.7242 & 0.6995 & 0.3067 & 0.7577 & 146 & 36.8 \\ \hline
3 & UNet & 0.7054 & 0.6165 & 0.1977 & 0.6855 & 730 & 3.2 \\ \hline
4 & FCN & 0.652 & 0.5285 & 0.1722 & 0.5876 & 327 & 44.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The table shows the results of the constant experiment, where threshold x 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. FPS indicates the number of images that our experimental platform can process per second, and model size indicates the size of the trained model weights. Figure 9: The figure shows the OKS scatter plot of the constant experiment, with the horizontal coordinate showing the different OKS thresholds and the vertical coordinate showing the proportion of test samples meeting the current OKS threshold to the total test samples. FPS indicates the number of images that our experimental platform can process per second, and model size indicates the size of the trained model weights. Table 2 shows that our proposed algorithm has the best performance, and on the scatter plot, our proposed algorithm keeps leading in terms of the ratio at different thresholds. Using FCN-32S as the backbone has the worst result, with a difference of 0.2371 from the accuracy rate of our proposed addition algorithm. This is because, in the up-sampling part of FCN-32, it uses a transposed convolution with a step size of 32 to upsample the feature map 32 times, reducing it to the original map size. However, the disadvantage of the FCN-32S model is that the final feature map is upsampled 32 times at once during the up-sampling process, at which time a lot of details are lost during the up-sampling process because the feature map of the last layer is too small. Compared with FCN-32S, UNet performs better because the up-sampling rate of UNet is controlled at 2 times, and the down-sampling rate is controlled at 0.5 times, which is the same setting as our proposed DRUNet. Therefore, for small target detection tasks, the appropriate up-sampling and down-sampling rates are very important, and the sampling rate should not be too large.