# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results
### Experimental Results
#### 4.2.2 The Setup of Proxy Classification Tasks  
In the previous Section, by comparing the false alarm rate performance of detectors trained using three different strategies in their respective experiments, we concluded that the DCD training strategy can yield detectors with better detection performance, especially  
Figure 12: Missing alarm rate–false alarm rate curve among Detectors A, B and C.  
in terms of improving the false alarm rate. This optimization is attributed to setting the proxy task as a classification task, which enhances the backbone network's ability to distinguish between targets and backgrounds. Detector C and Detector F are the detectors obtained from Experiment 1 and Experiment 2 using the DCD training strategy, respectively. The training results of the classification tasks corresponding to these two detectors are shown in Figure 13. In Figure 13b, the \"accuracy_top5\" is consistently 1 because the SAS450 dataset contains only three target classes, and with the background included, its derived classification dataset has a total of four categories. Therefore, the top five predictions in the proxy task during the training process of Experiment 2 will always include the correct label, hence the \"accuracy_top5\" in Figure 13b is consistently 1. Detectors C and F are the detectors with the best false alarm rate performance in their respective experiments, which also suggests that the DCD training strategy adopted in this paper is robust when applied to sonar images collected at different frequencies. Influenced by external factors and considering the dataset size, image quality, and target categories, the quality of the SAS450 dataset is lower than that of the SAS240 dataset. This results in the classification dataset derived from SAS450 being of inferior quality compared to the one derived from SAS240. Therefore, we designed Experiment 3 to attempt using the backbone network trained on the higher-quality derived dataset, SAS240, for learning and inference based on the SAS450 dataset. The detector obtained through this process is denoted as Detector G. The results of Detectors F and G on the test set are shown in Figure 14.  
Comparing the false alarm rate performance of Detectors D, F, and G on the SAS450 test set, we recorded the detectors' false alarm rates and miss rates when the IoU between the detection results and the annotations is greater than or equal to 0.5, and the confidence levels are taken at intervals of 0.05 within the range [0.05, 0.4]. The results are shown in Figure 15.  
Figure 13: The training results of the classification task: (**a**) results of SAS240; (**b**) results of SAS450. It can be seen that both Detector F and Detector G exhibit lower false alarm rates than Detector D across the entire confidence interval. At low confidence levels, Detector F outperforms Detector G in terms of both false alarm rate and miss rate. However, as the confidence level increases, the false alarm rate and miss rate of Detector F gradually converge towards those of Detector G. The false alarm rate-miss rate curves for Detectors D, F, and G within the confidence interval are shown in Figure 16. It can be observed that the curves for Detector F and Detector G are very close. Although the curve for Detector G is closer to the origin of the coordinate axis, its false alarm rate performance is relatively  
Figure 14: The detection results of Detectors F and G on the SAS450 test set. (The yellow bounding box indicates a false alarm, and the red bounding box indicates a correct detection). Figure 15: The performance of Detector D, Detector F and Detector G on the SAS450 test set: (**a**) The comparison of false alarm rates; (**b**) The comparison of miss rates. unstable. While the curve for Detector F is farther from the origin than that of Detector G, it is concentrated within a certain range and is more stable.