# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 1 Introduction  
Imaging sonar is an effective detection payload for underwater unmanned systems, and sonar image object detection can provide critical attributes such as target location, category, and size for these systems, making it a key technology in the technical framework of underwater unmanned systems. In recent years, deep learning has made significant progress in the field of image object detection, prompting deep learning-based sonar image object detection technology to quickly become a prominent research hotspot in the field of underwater perception both domestically and internationally [1, 2, 3, 4, 5, 6]. In practical sonar image detection tasks, deep learning detectors commonly face the challenge of high false alarm rates due to issues such as the scarcity of target image samples in sonar images and the difficulty in constructing high-performance deep convolutional neural networks [7]. Compared to optical cameras, sonar is affected by various factors such as clutter, reverberation, ship speed, and beam tilt during the imaging process, resulting inlow signal-to-noise ratio, signal-to-interference ratio, and signal-to-clutter ratio in sonar images [8; 9; 10]. Therefore, deep learning object detection methods that perform well on optical images do not yield high output classification confidence when directly applied to sonar images. To improve recall rates, the detection threshold of deep learning detectors is often lowered, but this also leads to higher false alarm rates. High false alarm rates severely constrain the effectiveness of underwater unmanned systems in executing critical tasks such as navigation, target seeking, and detection. There are mainly two ways to reduce the false alarm rate of deep learning detectors: post-processing suppression [11; 12] and improving the detector's generalization and adaptability [5; 13; 14]. Post-processing suppression refers to filtering detection results by introducing expert knowledge. [PERSON] et al. proposed a false alarm reduction method based on probability maps, but this method is specific to certain scenarios and introduces additional information [12]. Although it improves the issue of high false alarm rates, it has high requirements for hardware and professional knowledge and is not universally applicable. Improving the adaptability of deep learning detectors refers to enhancing the detector's adaptability to sonar images by optimizing detector design and training, thereby achieving low false alarm rates. There are usually two solutions to improve detector adaptability. One is to use a larger dataset, but publicly available sonar image datasets are scarce in the field of underwater detection, so data augmentation methods are commonly used to expand the dataset [14; 15]. However, sonar image datasets are often small-scale, and overlearning of samples through data augmentation can easily lead to overfitting. The other is to optimize the detector's model. [PERSON] et al. adaptively designed a sonar image detector structure using a neural network architecture search for sonar image datasets [5], while [PERSON] et al. modified the loss function in their research [13]. Although these methods can reduce false alarm rates even with low-quality datasets, they rely on the professional experience of researchers and have low compatibility with different scenarios. False alarms occur when the background is mistakenly identified as the target, which is caused by incomplete learning of the target features. In self-supervised learning, researchers design an indirect task for learning the semantic information or other features of images. The setup of proxy tasks is usually related to downstream tasks, but in a simpler form, or it is easier to obtain supervisory information from unlabeled data. By solving proxy tasks, the model can learn the intrinsic structure and features of the data, thereby improving its performance on downstream tasks [16; 17; 18; 19]. [PERSON] et al. used automatic colorization as a proxy task to improve classification and segmentation tasks [17].