The limitations of underwater acoustic communications mean that the side-scan sonar data of an autonomous underwater vehicle (AUV) cannot be transmitted back and processed in real time, which means that targets cannot be detected in real time. To address the problem, this paper proposes an autonomous underwater vehicle-based side-scan sonar real-time detection method for underwater targets. First, the paper describes the system and operation of real-time underwater-target detection by the side-scan sonar mounted on the autonomous underwater vehicle. Next, it proposes a real-time processing method for side-scan sonar data, method for constructing a deep-learning-based underwater-target detection model, and real-time method for underwater-target detection based on navigation strip images, which, together, solve the three key technical problems of real-time data processing, deep-learning-based detection model construction, and real-time target detection based on the autonomous underwater vehicle. Finally, through sea-based experiments, the effectiveness of the proposed methods is evaluated, providing a new solution for the autonomous underwater vehicle-based side-scan sonar real-time detection of underwater targets.

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Although target detection algorithms based on deep learning have achieved good results in the detection of side-scan sonar underwater targets, their false and missed detection rates are high for multiple densely arranged and overlapping underwater targets. To address this problem, a side-scan sonar underwater target segmentation model based on the Blended Hybrid dilated convolution and Pyramid split attention U-Net (BHP-UNet) algorithm is proposed in this paper. First, the blended hybrid dilated convolution module is adopted to improve the ability of the model to learn deep semantics and shallow features while improving the receptive field. Second, the pyramid split attention module is introduced to establish a long-term dependency between global and local information while processing multi-scale spatial features. Three sets of experimental results show that the BHP-UNet model proposed in this paper has better segmentation performance than the conventional fully convolutional network, U-Net, and DeepLabv3+ models, and it is able to segment dense and overlapping targets to a certain extent. The proposed model will have significance as a guide for practical applications.

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Given the lack of systematic research on bathymetric surveys with multi-beam sonar carried by autonomous underwater vehicles (AUVs) in unfamiliar waters, this paper proposes a method for multi-beam bathymetric surveys based on the constant-depth mode of AUVs, considering equipment safety, operational efficiency, and data quality. Firstly, basic principles for multi-beam bathymetric surveys under the constant-depth mode are proposed based on multi-beam operational standards and AUV constant-depth mode characteristics. Secondly, a vertical effective height model for the vehicle is established, providing vertical constraints and a basis for determining fixed depth in constant-depth missions. Subsequently, according to these basic principles and the vertical effective height model, the operational process for multi-beam bathymetric surveys in unfamiliar waters under the AUV constant-depth mode is outlined. Finally, we validate the proposed method through sea trials in the Xisha Sea of the South China Sea. The test results show that the method proposed in this paper not only ensures the vehicle safety operation and multi-beam data quality, but also improves the operation efficiency by about 68%, demonstrating the reliability of the proposed method and its significant engineering value and guidance implications.