# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results  
### Experimental Settings  
#### 4.1.1 Data Preparation  
Using deep learning methods for automatic target recognition based on sonar images is a popular research direction in the field of underwater recognition. However, due to the difficulties in collecting sonar images of underwater targets and the scarcity of public datasets, many deep learning models that perform well on optical images do not yield satisfactory results when transferred to process sonar images, mainly manifested by high false alarm rates and low accuracy. On the one hand, mainstream deep learning detection methods today rely on large-scale datasets, which are precisely what is lacking in the field of sonar image detection. On the other hand, there is a domain gap between optical images and sonar images, leading to poor performance. This paper collects and produces two datasets: SAS240 and SAS450. The SAS240 dataset contains 91 side-scan synthetic aperture sonar (SAS) images, which were collected in a lake using SAS from Mybro at a frequency of 240 kHz. These sonar images will be sliced into sizes suitable for training during the dataset creation process, which are shown in Figure 5. The SAS240 dataset includes six types of targets: cylinder, rectangle, cone, globe, tyre, and mine. The SAS450 dataset contains 60 side-scan synthetic aperture sonar images, which were collected in the sea area using SAS from Mybro at a frequency of 450 kHz. These sonar images are sliced into sizes suitable for training during the dataset creation process, as shown in Figure 6. The SAS450 dataset includes three types of targets: cylinder, cone, and tyre. Figure 5: Targets and background in datasets SAS240. Figure 6: Targets and background in datasets SAS450. It should be noted that the \"cylinder\" and \"cone\" targets in SAS240 and SAS450 are not entirely consistent. Although they belong to the same type of target in terms of shape, they are made of different materials. The \"cylinder\" and \"cone\" in SAS240 are made of metal, while those in the SAS450 are made of glass fiber reinforced plastic, which results in different acoustic scattering characteristics. This means that their feature representations in sonar images are different. In addition to the targets, the data collection scenarios also include shallow water reverberation, reefs, and some man-made objects. The appearance of these objects in sonar images can easily cause false alarms in target detection algorithms, making them suitable for evaluating the false alarm rate of deep learning algorithms. The datasets SAS240 and SAS450 are divided into training, validation, and test sets in ratios of 2:1:1 and 7:2:1, respectively. The sonar images in both datasets are cropped with a step size of 0.5, resulting in a commonly used image size of 640 \(\times\) 640 pixels for detection tasks. The image files are in the standard JPG format. All images in the datasets have been manually annotated, with annotation files in the YOLO format, which are text files that include the image coordinates of the bounding boxes and the object categories. This allows for rapid deployment for object detection tasks. Finally, the distribution of object categories and sizes in the training and validation sets of both datasets are shown in Figure 7.  
Although the sonar dataset covers multiple types of underwater targets, there is an imbalance in the distribution among different target classes. In SAS240, there are too many globes and too few mines, while in SAS450, there are an excessive number of cylinders. This situation arises mainly due to two reasons. Firstly, the difference in production costs among targets results in a higher number of easily obtainable targets being deployed during the process, leading to an excessive collection of such targets. Secondly, during the data acquisition process, some targets are located near the blind spots of the side  
Figure 7: Distribution of target category quantities in the dataset.