# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 3 Materials and Methods  
### Overall Idea  
Currently, popular detectors such as Faster R-CNN [27], YOLOs, SSD, and RetinaNet [28] follow the \"pre-trained-fine-tune\" paradigm of transfer learning to achieve high generalization on general detection tasks and accelerate the training process, thereby achieving good performances on test datasets. However, compared to optical images, sonar images suffer from lower image quality on one hand, due to various unavoidable factors such as clutter, reverberation, and ship speed affecting the sonar during the imaging process. On the other hand, because the scattering characteristics of sound differ from those of light, the features of objects presented in sonar images and optical images are visually distinct. Therefore, researchers have not achieved the expected ideal localization and recognition results when they reuse the feature extraction parts of models pre-trained on large optical image datasets such as COCO and ImageNet for detection tasks based on sonar images. For this reason, some researchers need to carefully select hyper-parameters based on experience when training deep learning models using the transfer learning paradigm, to avoid overfitting during fine-tuning, which places high demands on the researchers' familiarity with acoustic theory.  
In the field of underwater target detection, it is difficult to pre-train the feature extraction part of models on large scale sonar image datasets, which is due to the challenging acquisition of sonar images and the scarcity of existing open-source sonar datasets. Some researchers have attempted to use data augmentation methods to expand datasets for training, but some generated sonar images often have issues where the shadow relationships of targets do not conform to the characteristics of acoustic scattering. In addition to the lack of public datasets, another problem is the variation in target samples among sonar images acquired from different water bodies. These issues prevent the automatic achievement of better regularization results when using external datasets for pre-training in underwater target detection tasks based on sonar images.  
The simplest solution to this problem is to directly train the detection model on the collected sonar image dataset through self-training. However, due to the lack of initial weights provided by a pre-trained model and the excessive number of parameters that the detector needs to learn during training, this training method often results in weak feedback to the backbone network during training, leading to a dispersion of the model's attention.  
To enable the backbone network to learn feature representations suitable for the task dataset, this paper proposes a new training framework, as illustrated in Figure 2 in comparison to traditional frameworks. This paper suggests setting up a proxy task to provide an appropriate supervisory signal for the backbone network to learn feature representations from the task data. The resulting backbone network will exhibit higher generalization performance in downstream tasks, thereby reducing the false alarm rate in target detection tasks. The choice of proxy task can be diverse. This paper proposes setting the proxy task as a classification task, where the classification dataset is derived from the detection dataset. In addition, the proxy task can also be set as other tasks, which is work that we will undertake in the future. The specific details of the training strategy based on the classification proxy task will be elaborated in subsequent Sections.