# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results
### Experimental Results
#### 4.2.4 Analysis of Evaluation Metrics  
In addition to analyzing the performance of the three types of detectors on the test set, this paper also compares commonly used evaluation metrics in object detection algorithms, with the mAP results shown in Table 3. On the SAS240 dataset, whether it is [EMAIL_ADDRESS] or [EMAIL_ADDRESS]-0.95, the results obtained using the DCD strategy for training are superior to the other two methods, with improvements of 1.46% and 6.09% compared to the pre-trained-fine-tune strategy, and improvements of 0.78% and 3.83% compared to training from scratch, respectively. However, on the SAS450 dataset, whether it is [EMAIL_ADDRESS] or [EMAIL_ADDRESS]-0.95, Detector F trained using the DCD strategy performs worse than Detector D, with decreases of 9.11% and 19.94% compared to Detector D, respectively. Furthermore, both [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 of Detector G are significantly lower than those of Detector H, with decreases of 47.66% and 53.32%, respectively. This result is completely inconsistent with their performance on the test set, leading us to believe that mAP should not be used as the primary metric for evaluating algorithm performance in the field of sonar-based object detection. To comprehensively consider both the detector's ability to detect all targets and its ability to correctly detect targets, this paper compares the F1-Score. In practical detection tasks, the detection threshold is often selected by referring to the F1-Score at different  
Figure 17: The detection results of Detectors G and H on the SAS450 test set. (The yellow bounding box indicates a false alarm, and the red bounding box indicates a correct detection). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detector** & **mAP50** & **mAP50-95** \\ \hline Detector A & 0.893 & 0.46 \\ Detector B & 0.899 & 0.47 \\ Detector C & 0.906 & 0.488 \\ Detector D & 0.516 & 0.326 \\ Detector E & 0.379 & 0.147 \\ Detector F & 0.469 & 0.261 \\ Detector G & 0.413 & 0.225 \\ Detector H & 0.789 & 0.482 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: mAP comparison for Detectors. confidence levels. Once the threshold is determined, the F1-Score becomes a fixed constant. Therefore, this paper only compares the maximum F1-Score. Assuming that there is a maximum F1-Score, where the confidence level is T, this paper also compares the false alarm rate performance of different detectors on the test set at this confidence level and contrasts it with the average false alarm rate of the detector. The results are shown in Table 4.  
The F1-Score ranges between [0,1], with values closer to 1 indicating a more perfect balance between precision and recall for the model, and values closer to 0 indicating that at least one of the precision or recall metrics is very poor. Generally, when the confidence T corresponding to the maximum F1-Score is selected for each of the three detectors, they can exhibit their best detection performance while balancing false alarms and missed detections. Therefore, it is meaningful to compare the false alarm rates \(P_{fa_{T}}\) of each detector at this point. As shown in Table 4, Detector C and Detector F have the lowest false alarm rates among their respective groups of detectors. Detector C has a lower false alarm rate by 5.61% and 26.74% compared to Detector A and Detector B, respectively. Detector E is not discussed due to its failure on the test set. Detector F has a lower false alarm rate by 19.22% and 6.19% compared to Detector D and Detector H, respectively. When comparing the false alarm rates of detectors tested on the SAS450 dataset, it is found that the detector trained using our strategy exhibits a significant reduction in false alarm rate. This phenomenon can be attributed to two factors. On one hand, the smaller scale of the SAS450 dataset makes the reduction in false alarms more pronounced in percentage terms.