RepDNet:A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.

Underwater Terrain-Aided Navigation Based on Multibeam Bathymetric Sonar Images

Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map, This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge comer pixels are then defined and used to construct an edge comer histogram, which employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.

DcNet: Dilated Convolutional Neural Networks for Side-Scan Sonar Image Semantic Segmentation

In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.

YOLOv5-Based Seabed Sediment Recognition Method for Side-Scan Sonar Imagery

Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.

Multi-beam Sonar and Side-scan Sonar Image Co-registering and Fusing

Multi-beam Sonar and Side-scan Sonar compensate each other. In order to fully utilize all information, it is necessary to fuse two kinds of image and data. And the image co-registration is an important and complicated job before fusion. This paper suggests combining bathymetric data with intensity image, obtaining the characteristic points through the minimal angles of lines, and then deciding the corresponding image points by the maximal correlate coefficient in searching space. Finally, the second order polynomial is applied to the deformation model. After the images have been co-registered, Wavelet is used to fuse the images. It is shown that this algorithm can be used in the flat seafloor or the isotropic seabed. Verification is made in the paper with the observed data.

Sedimentary processes in Zenisu deep-sea channel revealed by side-scan imagery

Side-scan sonar data collected by Cruises 99-09 Leg 2 and 00-06 Leg l of R/V Yokosuka were used to reveal the sedimentary processes in Zenisu deep-sea channel. The middle and lower segments of the channel are rich in turbidite and other debrite deposits. By high-resolution imaging, three sedimentary processes were distinguished with distinct acoustic features. 1. Slumps and slides occur with contrasting backscatter, rough surface textures, blockings, and acoustic shadows at headwalls. They are very extensive and often in lobate form downslope. 2. Debris flow has uniform, general medium backscatter, sometimes showing marbling/lineation in lobate form. 3. Turbidity current is characterized by low backscatter confined to the channel as acoustic signal is attenuated. Regional tectonics must be the dominating factor that controls deposition pattern in this area.

基于测深侧扫声呐的DOA估计算法研究

针对测深侧扫声呐进行波达方向(Direction of Arrival,DOA)估计时会受到阵元幅度、相位误差及低信噪比影响的问题,提出一种改进的波束域加权子空间拟合算法。首先,采用总体最小二乘-旋转不变子空间算法进行回波方向预估计;其次,将连续线阵划分为多个子阵,并将各个子阵在预估计方向做加权波束形成;再次,采用加权子空间拟合(Weighted Subspace Fitting,WSF)算法构造代价函数;最后,采用阻尼牛顿法求解得到高精度的DOA估计结果。仿真结果表明,文中所提算法在阵元出现幅度相位误差条件下的角度估计均方误差相对于WSF算法减少了约0.03°。海试数据分析结果表明,文中所提算法的测深点均方误差整体优于WSF算法,其相对测深精度提高了约9.8个百分点。以上分析结果表明,文中所提算法整体优于WSF算法,可以实现在阵元幅度相位误差及低信噪比情况下的高精度DOA估计。

DTA-6000声学深拖系统在富钴结壳探测中的应用

富钴结壳作为一种潜在的海洋矿产资源逐渐引起人们的关注,研究发现海山微地形是影响富钴结壳分布的重要因素。DTA-6000声学深拖系统是我国具有自主知识产权的第一套深海拖曳观测系统,其最大工作深度6 000m,该设备上安装的高分辨率测深侧扫声纳和浅地层剖面仪能够分别获得高分辨率的海底地形地貌和浅地层剖面。它的测深覆盖范围600m,侧扫覆盖范围800m。声学深拖系统因其高效和价格优势,被列为富钴结壳资源调查的常规设备。大洋29航次中,DTA-6000声学深拖系统在采薇海山完成2条测线共约50km海山斜坡的探测,获得了高分辨率的地形地貌数据和浅地层剖面数据。介绍DTA-6000声学深拖系统及其在富钴结壳探测中的应用,并对探测结果进行了分析。

高分辨率测深侧扫声纳

文中介绍了由中国科学院声学研究所和美国亚迪技术开发(上海)有限公司联合设计和制造的高分辨率测深侧扫声纳,它能够同时获得高分辨率的海底地形和地貌。该声纳由电子分机和分别安装在载体左右两侧的两条声纳阵组成,最大工作水深6 000 m。声纳阵由一条发射线阵和10条间距为λ/2的接收线阵组成,λ为声波波长,其中8条线阵接收声信号,两边的两条为哑元。声纳的多子阵海底自动检测-子空间拟合信号处理方法能克服水声信道多途和复杂海底的影响,正确检测到海底的直达回波。2003年11月和2004年7月,声纳在中国浙江千岛湖进行两次长时间的湖试,获得了高分辨率湖底等深线图和地貌图,正确检测出湖底边长为0.5 m立方体目标的高度。

Geoswath多波束系统及其在航道测量中的应用

介绍Geoswath多波束系统的基本组成和相干声纳的工作原理，与常规多波束系统相比，该系统具有数据密度大、水深分辨率高、条幅中间数据密度相对较低等特点。系统参数测试中，使用交叉点法获取纵倾偏差和艏摇偏差的方法，提高参数测试的精度。在经过各种校准后，对水深变化在几米至30多米的航道进行测量，在局部地形图中清楚地显示水深相对高差约20cm，大小1～2m的地形起伏，能较为清楚地显示航道经过不同形式改造的痕迹。

浅水多波束测深声纳关键技术剖析

基于多波束测深声纳工作原理,结合国内外主流浅水多波束测深声纳产品与技术发展现状,分析了浅水多波束测深声纳具备的5种典型能力,提出了从能力表征的角度理解浅水多波束测深声纳性能指标;总结了保障这些能力所采用的主要关键技术,讨论了技术实现的具体思路,列举了仿真或试验数据处理结果;展望了多波束测深声纳的发展趋势。

基于声波探测的海底管道非掩埋状态检测方法

基于声波探测技术,提出一种对海底管道非掩埋状态进行联合检测的方法,以山东胜利油田某海底输油管道为对象,设计一种双声纳探头多波束测深系统,并联合侧扫声纳系统,通过对系统参数进行分析、优化,从而实现对海底管道非掩埋状态的高效精确检测。分析检测结果表明:基于双声纳探头多波束系统的海底管道检测可获得高密度的点云数据,显著提高了管道检测数据的稳定性及可靠性;侧扫声纳系统的良好检测效果有赖于其声波掠射角的最佳取值;参数优化后的联合检测系统的检测效率得到显著提高,能够精确判断出管道的实际状态。

基于空间平滑的多波束测深声呐相干分布源方位估计

在多波束测深声呐的工作环境中,若海底反向散射信号不满足点源假设,方位估计精度将严重下降,而基于分布源模型的方位估计算法可以适应这种环境。目前,大多数分布源算法要求分布源之间不相干,有人提出采用Toeplitz方法估计相干分布源,但该方法精度不高并且忽略了角度扩展参数。为解决多波束测深声呐相干分布源的方位估计问题,提出了基于空间平滑的广义MUSIC方法,公式推导证明了算法的有效性,通过计算机仿真给出算法方位估计的精度以及不同信噪比条件下的性能,最后采用多波束测深系统的实验数据对算法进行了验证。

高分辨率测深侧扫声纳系统测深精度评估方法

高分辨率测深侧扫声纳系统(HRBSSS)是一种用于获取海底微地形地貌的声学探测设备。在简要介绍其工作原理、主要误差源、数据处理与偏差校准方法的基础上,针对声纳自身与整个系统两方面确定了各自的测深精度评估方法。最后以DTA-6000声学深拖系统上的HRBSSS为例,对其外场实验测深数据精度进行了评估。结果表明,HRBSSS声纳的测深精度在250m×2覆盖范围内符合IHO S-44特级标准,受传感器误差和界面影响,浅水条件下系统测深精度在水平距离113m内符合IHO S-44特级标准。

侧扫声纳声线反射和折射干扰的实验分析

介绍了侧扫声纳由自身声源反射和折射衍生的两种干扰波的形成机理,提出以绘制声线图进行分析的研究方法,着重通过海上实作验证了温度跃层产生的声线弯曲对声纳的影响特点,为外业工作的设计和施测提供了几点建议。

基于盖氏圆准则的相关信源数估计研究

针对均匀线阵提出了一种适用于小阵列去相关处理的盖氏圆准则应用方法。这种方法使盖氏圆准则在不减少阵列有效孔径的前提下,仍然可以对有相关性的信源数得到准确估计。从理论上证明了这个方法的可行性,并且把这个方法运用到浅水高分辨率测深侧扫声纳对附近船只尾流干扰信号的检测中,取得了比较好的效果。

深水多波束测深系统脉冲压缩实时算法

深水多波束测深系统是一种高效率、高精度和高分辨率的海洋调查勘测仪器。针对深水多波束测深系统中脉冲压缩运算量大的问题,提出了一种基于分组的脉冲压缩实时算法。该算法对需要进行脉冲压缩的波束进行分组,每个节拍只计算一组波束,可大幅减少深水多波束测深系统脉冲压缩的运算时间,节省了处理器资源。该算法已成功应用于某深水多波束测深系统,经多次海试证明,工作稳定可靠。

基于多波束测深声呐的水中气体目标检测方法

针对水中气体目标检测存在的水下环境复杂、探测目标多等问题,本文提出一种基于多波束测深声呐的水中气体综合检测方法。针对水中气体目标静态和动态特征,综合利用了一维波束输出、二维图像输出和三维声呐图像序列信息,在波束域上利用自适应阈值多次检测算法检测水体目标;在图像域中,采用数学形态学提取目标轮廓;针对三维声呐图像序列,利用尺度不变特征流估计气体目标的运动特征。水池和外场实验数据表明:自适应阈值提高了多次检测算法的鲁棒性;基于稠密匹配和图像金字塔理论的SIFT Flow算法能够从多波束测深声呐图像序列中估计气体目标运动特征,为水中气体目标的分布规模定量评估奠定基础。

利用测深侧扫数据和明暗恢复形状方法实现地形修正

一般的测深侧扫声纳应用中,单独利用回波数据的幅度信息或相位信息获取侧扫图或测深图以展示海底细节特征。为提取侧扫数据中的微地貌信息,实现更高精度的海底地形探测,提出了两步循环迭代算法:首先利用原始测深侧扫结果数据对散射模型进行最优拟合,其次,引入亮度误差修正因子,改进从明暗恢复形状算法并迭代地形,保证其快速稳定的收敛,最终通过循环迭代获取了海底底质参数和精度更高、与真实地形起伏相关性更强的地形深度值。同时,利用Jackson海底散射模型,模拟测深侧扫声纳信号的发射接收过程,并利用其回波数据,验证本迭代算法的正确性和有效性。结果表明:该方法可以有效地修正地形,且接收信噪比越高,地形修正效果越好;在信噪比为20 d B时,相比于原始测深结果,修正后地形起伏相关系数提升52. 4%,地形误差绝对值降低37%。最后,将该算法应用于测深侧扫声纳数据,通过修正前后地形图的对比分析,验证了本算法的可行性和有效性。

一种海底浅层声学探测数据综合可视化方法

提出了一种基于Open GL可编程管线的海底浅层声学探测数据三维综合可视化方法。通过处理,将侧扫声呐影像、多波束数据和浅地层剖面在同一视图下显示,可以方便的对海底地质环境多种信息进行综合判读并进行多维数据的交互式提取。利用纹理缓冲区处理侧扫声呐影像数据,具有数据加载量大的优点,避免了实际应用中纹理数据反复切换带来的延迟;并且探讨了侧扫声呐影像和多波束数据分辨率不一致引起的纹理贴图问题。该方法在南海海底峡谷区域的海底地质环境综合显示和分析中进行了应用,结果表明,该方法能处理多种格式的侧扫声呐影像,不受侧扫声呐影像和多波束测深数据分辨率不一致的限制,数据加载量大、绘制速度快。