# DRUNet: A Method for Infrared Point Target Detection
## 5 Experiments
### Ablation Study  
To verify the effectiveness of the attentional enhancement module and residual structure we used in each node, we set up several variants as ablation experiments. In the ablation experiment, we propose to verify four questions. * Question 1: Is the attention enhancement module effective, and how much can it improve performance? * Question 2: Is the ECA module more suitable than other more complex attention enhancement modules for the detection of weak IR targets? * Question 3: How much performance optimization can be achieved by using only the residual module for each block without using any attention enhancement module? * Question 4: Does deep supervision result in improved network performance? To verify these problems, five experiments were set up, the ablation experiments with the same detection head and all the parameters set in the training process. The difference only lies in the backbone. The specific settings are the same as in the TRAINING METHOD. The results are shown in Figure 7. The horizontal coordinate is the threshold value of OKS in the range of [0, 1], and a total of 40 points are set. The vertical coordinate is the ratio of the number of samples with OKS greater than the threshold to the total number of samples under the current threshold, and the algorithms corresponding to the different results can be seen in the legend. Figure 8 shows an example of Experiment 1 results, and the green dots indicate the positions of the predicted coordinates. As shown in the figure, the green dots can cover the original target. Table 1 shows the results of the ablation experimental measurements, and the values representing the best performance are indicated in bolded font. For better quantitative analysis, we extracted three thresholds to create the table, with thresholds of 0.5, 0.75, and 0.95. Figure 8: These pictures show the effect of the predicted coordinate tracing points, and it can be seen that the offset of the network prediction results is small and can overlap with the small target. Figure (**a**) is from the SIRST dataset, and its size is less than 256 \(\times\) 256. To not change the size of the small target in the picture, it is changed to 256 \(\times\) 256 by padding. Figure (**b–d**) from ground/air background infrared image small aircraft target detection tracking dataset. Figure 7: The figure shows the OKS scatter plot of the ablation 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. The overall trend of Figure 7 shows that our proposed algorithm performs best and has more predicted samples that satisfy the threshold than the other models in the ablation experiment regardless of the OKS threshold. This shows that the network is generally good at the task of predicting the location of the point target. Because of the dense structure and the lightweight ECA attention mechanism, each block in the feature extraction network can share part of the weights, so although the model is relatively deep, the model size is only 10.5 MB and the inference speed is relatively fast. The comparison of Experiments 1, 2, and 3 can answer question 1. The inclusion of the attention mechanism is effective, and with other quantities held constant, Experiments 1 and 2 have higher accuracy rates of 0.0928 and 0.0722, respectively, than the Experiment 3 accuracy rate without any attention mechanism. OKS scatter plots, on the other hand, visually respond to the greater advantage of Experiments 1 and 2 over the results of Experiment 3. The comparison of Experiments 1 and 2 answers question 2.