# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 5 Conclusions and Discussions  
The false alarm rate is a crucial metric in unmanned underwater target detection tasks, yet few scholars have specifically discussed how to reduce it in existing research. Popular deep learning training methods in the field of underwater target recognition are limited by the quality of sonar images and the imaging differences between acoustic and optical images, making it difficult to achieve good results on real-world sonar image test data. We suggest setting the proxy task as a classification task and using this DCD training strategy to learn target and background features. Three experiments are designed in this paper to allow us to discuss our method. Experiment 1 is conducted on the SAS240 dataset, using the same sonar, during the same time period, and at the same location, with imaging results of the same batch of targets. The training and test samples are randomly divided. By comparing the performance of detectors trained using three different strategies on the test set, the effectiveness of the DCD training strategy is verified. The detector trained with DCD achieves a 3.91% reduction in false alarm rate at the same or higher recall rates. Experiment 2 is designed the same as Experiment 1, but uses the SAS450 dataset, which was collected with a different sonar frequency than the dataset in Experiment 1. In Experiment 2, the detector trained with the DCD strategy achieves an 18.50% reduction in false alarm rate at the same or higher recall rates. This once again verifies the effectiveness of the DCD strategy and demonstrates its robustness to sonar images collected at different frequencies. Experiment 3 explores self-training with weight transfer on sonar images collected under varying conditions including different frequencies, collection times, locations, and targets. The self-training process is conducted on the SAS240 dataset, while the learning and inference tasks are performed on the SAS450 dataset. The result shows a 14.31% reduction in false alarm rate at the same recall rate, validating the adaptability of self-training to different frequencies. Furthermore, in Experiment 3, the best-performing detector from Experiment 1 is used as the pre-trained model, and a popular training strategy is adopted to obtain a new detector. When compared under the same or higher recall rates, the detector obtained through self-training with weight transfer under the DCD strategy achieves a 19.61% reduction in false alarm rate. However, there are still some limitations in this study, mainly manifested in the following aspects. In terms of the dataset, some target samples in our sonar dataset are scarce, resulting in an uneven distribution of the dataset samples. To address this, we plan to conduct more experiments to collect additional sonar images and obtain a higher-quality dataset. Researchers can also consider improving this issue from the perspective of image preprocessing. In terms of models, we only trained the detector using the YOLOv8 model and did not experiment with more popular models. In our future work, we will consider using a wider range of more popular models for experimentation. Finally, in terms of algorithms, we only set the proxy task as a classification task in our current work. However, there are various choices for proxy tasks, such as segmentation tasks or noise reduction tasks. We will conduct relevant experiments in our future work to explore the effects of different proxy task settings on improving the false alarm rate. Conceptualization, P.Z. and Z.H. ([PERSON]); methodology, Y.F. ; software, [PERSON] ([PERSON]) and Z.Y. ; validation, J.H., [PERSON] ([PERSON]) and P.Z. ; formal analysis, J.H. ; investigation, Y.F. ; resources, Y.F. ; data curation, Z.H. ([PERSON]) and Z.Y. ; writing--original draft preparation, J.H. ; writing--review and editing, J.H. and P.Z. ; visualization, J.H. ; supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, P.Z. and Z.H. ([PERSON]).