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:To reduce the size and cost of an integrated infrared (IR) and green airborne LiDAR bathymetry (ALB) system, and improve the accuracy of the green ALB system, this study proposes a method to accurately determine water surface and water bottom heights using a single green laser corrected by the near water surface penetration (NWSP) model. The factors that influence the NWSP of green laser are likewise analyzed. In addition, an NWSP modeling method is proposed to determine the relationship between NWSP and the suspended sediment concentration (SSC) of the surface layer, scanning angle of a laser beam and sensor height. The water surface and water bottom height models are deduced by considering NWSP and using only green laser based on the measurement principle of the IR laser and green laser, as well as employing the relationship between NWSP and the time delay of the surface return of the green laser. Lastly, these methods and models are applied to a practical ALB measurement. Standard deviations of 3.0, 5.3, and 1.3 cm are obtained by the NWSP, water-surface height, and water-bottom height models, respectively. Several beneficial conclusions and recommendations are drawn through the experiments and discussions.Keywords: airborne LiDAR bathymetry; near water surface penetration; water surface height; water bottom height; suspended sediment concentration of the surface layer

An improved water-land discriminator using laser waveform amplitudes and point cloud elevations of airborne LIDAR.

Abstract:Affected by the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations, radiometric distortion degrades the quality of side-scan sonar images and seriously affects the application of these images. However, existing methods cannot correct distortion effectively, especially in the presence of seabed sediment variation. This study proposes a new radiometric correction method for side-scan sonar images that considers seabed sediment variation. First, the different effects on backscatter strength (BS) are analyzed, and along-track distortion is removed by establishing a linear relationship between distortion and sonar altitude. Second, because the angle-related effects on BSs with the same incident angle are the same, a novel method of unsupervised sediment classification is proposed for side-scan sonar images. Finally, the angle-BS curves of different sediments are obtained, and angle-related radiometric distortion is corrected. Experiments prove the validity of the proposed method.

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed.