# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results
### Experimental Results
#### 4.2.1 Performance on the Test Set
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Detector** & **Training Set of** & **Training Set of** & **Training Set** & **Training Strategy** \\ \hline \multirow{4}{*}{Experiment 1} & Detector A & Coco & \(-\) & SAS 240 & Pre-trained–fine-tune \\  & Detector B & \(-\) & \(-\) & SAS 240 & Train from Scratch \\  & Detector C & \(-\) & SAS 240 & SAS 240 & DCD \\ \hline \multirow{4}{*}{Experiment 2} & Detector D & Coco & \(-\) & SAS 450 & Pre-trained–fine-tune \\  & Detector E & \(-\) & \(-\) & SAS 450 & Train from Scratch \\ \cline{1-1}  & Detector F & \(-\) & SAS 450 & SAS 450 & DCD \\ \hline \multirow{2}{*}{Experiment 3} & Detector G & \(-\) & SAS 240 & SAS 450 & DCD \\ \cline{1-1}  & Detector H & SAS 240 & \(-\) & SAS 450 & Pre-trained–fine-tune \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Detectors trained in Experiment 1 and Experiment 2. In Experiment 1, as the confidence level increases, the false alarm rates of Detectors A, B, and C on the SAS240 test set gradually decrease. Among them, the false alarm rate of Detector A is lower than that of Detector B only at low confidence levels. Among them, Detector A has a lower false alarm rate than Detector B only at low confidence levels. This implies that when a pre-trained model trained on a large-scale optical dataset is directly applied to perform target detection tasks on sonar images in the SAS240 dataset, it does not achieve the same good performance as in optical images and may even fail under high confidence conditions, exhibiting a higher false alarm rate than training from scratch. Detector C performs well in terms of false alarm rate, consistently showing a lower false alarm rate than Detector A within the experimental confidence interval and maintaining a lower false alarm rate than Detector B throughout most of the confidence interval. Although Detector B sometimes exhibits a lower false alarm rate in the high confidence interval, it is not significantly lower than that of Detector C, while Detector C's false alarm rate in the low confidence interval is significantly lower than that of Detector B. Therefore, we can consider Detector C's false alarm rate performance to be the best in Experiment 1. Meanwhile, in Experiment 2, Detector F also exhibits a lower false alarm rate than Detector  
Figure 9: The detection results of Detectors A, B, and C on the SAS240 test set. (The yellow bounding box indicates a false alarm, and the red bounding box indicates a correct detection). D. This indicates that, compared to the currently popular paradigm of transfer learning, the training strategy proposed in this paper can train detectors that exhibit lower false alarm rates in sonar target detection tasks, and this method is effective on datasets with different sampling frequencies and sizes. Figure 11: The performance of Detector A, Detector B and Detector C on the SAS240 test set: (**a**) The comparison of false alarm rates; (**b**) The comparison of miss rates. Figure 10: The detection results of Detectors D, E, and F on the SAS450 test set. (The yellow bounding box indicates a false alarm, and the red bounding box indicates a correct detection). In addition to the false alarm rate, which is the focus of this paper, researchers also pay attention to the missed detections of targets by detectors in practical detection tasks. This paper also provides a simple comparison of the miss rates of each detector at different confidence levels. The results for Detectors A, B, and C are shown in Figure 11b, while the results for Detectors D and F can be found in Table 2.  
It can be observed that the miss rates of detectors C and F are consistently lower than those of detectors A and D, respectively, throughout the entire confidence interval. Detectors A and D represent the results of the pre-trained-fine-tune paradigm in Experiment 1 and Experiment 2, respectively.