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To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image. [ABSTRACT FROM AUTHOR]

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method. [ABSTRACT FROM AUTHOR]

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors. [ABSTRACT FROM AUTHOR]