When used to recognize side-scan sonar images of shipwreck targets, the Faster R-CNN model is time-consuming and has low efficiency and a high missed detection rate for small targets. Considering that existing datasets of side-scan sonar images of shipwreck targets are small, we propose a YOLOv3 model that can automatically recognize side-scan sonar images of shipwreck targets based on transfer learning. Based on the Darknet-53 network, we froze part of the convolutional layer of the YOLOv3 model trained on COCO dataset images, and conducted transfer learning. Multi-scale training of shallow feature fusion was done based on multi-scale feature fusion with Feature Pyramid Networks (FPN) support, and the proportion of recognized shallow features of shipwreck targets increased. Meanwhile, the parameters and sizes of target anchor boxes were reset using K-means clustering, which allowed us to improve the speed of target recognition and the precision of smaller target recognition and positioning. Lastly, the binary classification cross entropy function was used to improve the loss function of the YOLOv3 algorithm. Experimental results show that under the same recognition target, the average precision (AP) value of the YOLOv3 model based on transfer learning reached 89.49%, which is an improvement of 0.31% and 1.77%, respectively, compared with the Faster R-CNN model and the traditional YOLOv3 model. Moreover, the YOLOv3 model based on transfer learning had the highest harmonic mean (F1), reaching 90.71%, which is 3.96% and 1.63% higher, respectively, than the harmonic means of the Faster R-CNN and the traditional YOLOv3 model. Lastly, the traditional YOLOv3 model takes an average 0.17s to identify a target. In contrast, the Faster R-CNN model takes an average of 2.8s to identify a target. Hence, our transfer learning YOLOv3 model greatly improves detection efficiency, meets the needs of real-time target recognition, and ultimately has better overall performance than existing methods.

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.

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.

In response to the absence of standardized work practices, work safety measures, efficient work procedures, and suitable line planning methods for exploring seabed topography using autonomous underwater vehicles (AUVs) equipped with side-scan sonar systems, this paper proposes a design framework for side-scan sonar measurement technology based on AUVs. Detailed research has been conducted on line planning methods that differ significantly from those used in shipborne operations. The new line planning method was developed based on the performance characteristics of AUV equipment, the principles of motion modes, and shipborne side-scan sonar operations. The validity of the line planning method was verified through actual sea experiments using different types of AUVs in various terrains. The experimental results demonstrate that the new survey line planning method is distinct from existing AUV path planning methods and is highly applicable for conducting seabed topography exploration operations. Furthermore, the technical design scheme studied in this article is applicable in different sea areas and ensures the safety, operational efficiency, and data quality of AUVs. It holds significant importance for marine science engineering and practical applications. The article also features an analysis of successful data collected from AUVs equipped with side-scan sonar for conducting seabed topography exploration operations.

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 UNet (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, UNet, 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.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.