# 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  
The experiments were conducted on an RTX3090 platform. When using DCD (Domain-Specific Data Curation, or any specific training method denoted by DCD in this context) training, the YOLOv8 classification model was first trained on DCD to obtain pre-trained weights. Afterwards, the detector was trained by first loading the pre-trained detection model provided by YOLOv8, then loading the backbone weights obtained from the classification task, and subsequently freezing some of the backbone network parameters before training. When training the detector on the SAS240 dataset, the number of epochs was set to 300, the batch size was 8 or 16, and the number of frozen layers in the backbone network was from layer 0 to 8. When training on the (it appears there is a repetition here, assuming it is meant to be SAS450 or another dataset for clarity) SAS (let us assume SAS450 for differentiation) dataset, the number of epochs was set to 100, the batch size was 8 or 16, and the number of frozen layers was from layer 0 to 6. For ease of discussion, the detectors trained using different training strategies on different experiments were assigned numbers, as shown in Table 1.  
Figure 8: The relationship among different datasets used in the experiments. These detectors were tested on their respective test sets, with the results on dataset SAS240 and dataset SAS450 shown in Figures 9 and 10, respectively. When the IoU (Intersection over Union) between the detection result and the ground truth is greater than or equal to 0.5, it is considered that the bounding box has detected the target and is colored red; otherwise, it is regarded as a false alarm and is colored yellow. It is worth noting that when multiple bounding boxes detect the same target repeatedly, only the one with the highest confidence among the multiple bounding boxes is selected as the detection result and is colored red, while the remaining bounding boxes are considered as false alarms and are marked in yellow. It can be seen that Detector A, B, and C can produce detection results on the SAS240 test set even at high confidence levels, whereas Detector D, E, and F can only produce detection results on the SAS450 test set at low confidence levels. Among them, Detector E did not detect any targets and completely failed. Therefore, Detector E will not be analyzed in subsequent discussions. The comparison results of the false alarm rate performance of different detectors are shown in Figure 11a, which depict the variation of false alarm rate with confidence level for Detectors A, B, and C within the confidence interval [0.1, 0.8]. Considering that Detector D and F can only detect targets at low confidence levels on the test set and that Detector E is not compared due to its failure, the false alarm rate performances of both are presented in table form, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Detector** & **Confidence** & **False Alarm Rate** & **Missing Rate** \\ \hline \multirow{8}{*}{Experiment 2} & \multirow{8}{*}{Detector D} & 0.1 & 0.846 & 0.667 \\  & & 0.2 & 0.889 & 0.833 \\  & & 0.3 & 0.833 & 0.833 \\  & & 0.4 & 1 & 1 \\ \cline{2-6}  & \multirow{8}{*}{Detector F} & 0.1 & 0.692 & 0.333 \\  & & 0.2 & 0.667 & 0.333 \\  & & 0.3 & 0.800 & 0.667 \\  & & 0.4 & 0.750 & 0.667 \\ \hline \multirow{8}{*}{Experiment 3} & \multirow{8}{*}{Detector G} & 0.1 & 0.765 & 0.333 \\  & & 0.2 & 0.556 & 0.333 \\ \cline{1-1}  & & 0.3 & 0.571 & 0.667 \\ \cline{1-1}  & & 0.4 & 0.600 & 0.667 \\ \cline{1-1} \cline{2-6}  & \multirow{8}{*}{Detector H} & 0.1 & 0.800 & 0.833 \\ \cline{1-1}  & & 0.2 & 0.800 & 0.833 \\ \cline{1-1}  & & 0.3 & 0.750 & 0.833 \\ \cline{1-1}  & & 0.4 & 0.750 & 0.833 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of Detectors on the SAS450 test set.