渔业声学数据后处理软件现状评述与展望:以Sonar5-Pro为例

渔业声学数据解析是渔业资源声学调查研究和应用的关键所在。目前全球渔业声学数据后处理的代表性软件主要有挪威Sonar5-Pro和澳大利亚Echoview。以Sonar5-Pro为例,开展了5个方面的研究:1)回顾了该软件自1994年以来近30年的发展历史;2)介绍了软件对声学数据进行处理的总体思路,即前处理、数据分析和结果展示3个步骤及贯穿始终的数据检视功能,以及软件的5项重要设计理念;3)介绍了该软件9项代表性功能特性的实现思路和具体方法;4)以主流分析应用鱼类生物量分析过程为例,介绍了软件的数据处理流程;5)对该软件的3项核心关键技术,即多目标跟踪(multiple target tracking)、交叉过滤跟踪(crossfilter tracker)和图像分析工具(image analysis)进行了详细介绍。研究发现,一个成熟的渔业声学数据后处理系统庞大而复杂,涉及渔业、物理学和计算机多学科知识的融合,着力加强相关领域交叉学科人才培养,充分借鉴吸收国外已有先进理念和成熟技术,基于各种应用场景需求研发具有自主知识产权的分析软件,采用引进消化吸收再逐点突破最终集成创新的方式,可以作为未来提升中国渔业声学数据解析能力和水平的重要发展途径。

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.

如何利用Jenkins与Sonar提升产品内在质量

对于软件工程而言,我们的内外部质量是什么呢?对客户、最终用户、间接用户的需求满足程度即是产品的外部质量。软件的内部质量,即产品架构的合理性,可扩展性,代码的规范性,可读性,简洁度,组件重用等等,这些质量属性往往对客户是不可见的。在公司内对代码的编写要求遵守编码规范,对于设计要求符合基本的设计原则,这些都是软件的内部质量!

An Underwater Robot Inspection Anomaly Localization Feedback System Based on Sonar Technology

This article introduces an underwater robot inspection anomaly localization feedback system comprising a real-time water surface tracking,detection,and positioning system located on the water surface,while the underwater robot inspection anomaly feedback system is housed within the underwater robot.The system facilitates the issuance of corresponding mechanical responses based on the water surface’s real-time tracking,detection,and positioning,enabling recognition and feedback of anomaly information.Through sonar technology,the underwater robot inspection anomaly feedback system monitors the underwater robot in real-time,triggering responsive actions upon encountering anomalies.The real-time tracking,detection,and positioning system from the water surface identifies abnormal conditions of underwater robots based on changes in sonar images,subsequently notifying personnel for necessary intervention.

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.

Fast Segmentation Method of Sonar Images for Jacket Installation Environment

It has remained a hard nut for years to segment sonar images of jacket installation environment,most of which are noisy images with inevitable blur after noise reduction.For the purpose of solutions to this problem,a fast segmen-tation algorithm is proposed on the basis of the gray value characteristics of sonar images.This algorithm is endowed with the advantage in no need of segmentation thresholds.To realize this goal,we follow the undermentioned steps:first,calcu-late the gray matrix of the fuzzy image background.After adjusting the gray value,the image is divided into three regions:background region,buffer region and target regions.Afterfiltering,we reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts.Finally,the remaining noise is removed by morphological processing.The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar images quickly and effectively.Thus,the stable and feasible method is testified.

Improving Yolo5 for Real-Time Detection of Small Targets in Side Scan Sonar Images

Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.

Application Research of Sonar Detection Method in Melting Exploration at the Bottom of Piles

Karst landforms are widely distributed in China,and are most common in Yunnan,Guizhou and Guangxi.If the development of karst caves at the bottom of the piles cannot be accurately ascertained before the construction of bridge pile foundations,accidents such as hole collapse,slurry leakage,and drill sticking will easily occur.In this paper,the principle and method of sonar detection for detecting karst caves at the bottom of bridge piles was introduced,and the sonar detection data and the cave situation at the bottom of the pile during the construction process in combination with the case of Yunnan Zhenguo Highway Project was analyzed,which verifies the practicability and reliability of sonar detection method reliability.

声呐图像水下目标识别综述与展望

随着海洋资源开发和水下作业的增加,声呐图像水下目标识别已成为热门研究领域。该文全面回顾了该领域的现状和未来趋势。首先,强调了声呐图像水下目标识别的背景和重要性,指出水下环境复杂和样本稀缺增加了任务难度。其次,深入探讨了典型的成像声呐技术,包括前视声呐、侧扫声呐、合成孔径声呐、多波束测深仪、干涉合成孔径声呐和前视三维声呐等。接下来,系统地审视了二维和三维声呐图像水下目标识别方法,比较了不同算法的优劣,还讨论了声呐图像序列的关联识别方法。最后,总结了当前领域的主要挑战,展望了未来研究方向,旨在促进水下声呐目标识别领域的发展。

基于主动识别声呐的养殖塘南美白对虾探测与初步分析

为实现高效、快速和准确的虾类识别和行为观测,提出一种基于主动识别声呐(DIDSON)的养殖塘南美白对虾探测和行为分析方法。该方法利用无人艇搭载主动识别声呐(DIDSON)对养殖塘设置的8个观测点进行连续定点观测,利用基于ECHOVIEW构建的虾类识别计数模型对采集的图像数据进行识别提取,通过目标识别、目标提取、通量分析以及方向分析等方法获得养殖塘南美白对虾的初步行为特征。试验在8个观测点观测的虾群数量极值分别为251只和208只,均值为234只,单位时间内的虾群通量区间为[108,131]只/(min·m^(2)),不同点位虾群正向游塘的占比均大于85%,通量变化较小。结果显示,该方法可以观测到养殖塘内虾群存在规律性的游塘行为,相较于传统的水下视觉观察和被动声呐调查,有效解决了养殖塘中虾群行为观测的难题,为制定更加高效的虾塘饲养和管理方案提供科学的数据支持。

分布式动力吸能复合结构声纳透声窗设计研究

本文针对潜艇声纳透声窗低频水动力噪声控制需求,基于平板与声腔的耦合振动方程,利用声振传递矩阵、模态展开法以及湍流边界层脉动压力频率波数谱等,建立多层复合平板的水动力自噪声计算模型。根据动力吸振特性分析,形成分布式吸能单元控制下的平板振动噪声方程,评估分布式吸能复合结构透声窗水动力自噪声,通过循环水槽试验验证其水动力自噪声降噪效果,为新型低噪声透声窗设计提供技术支撑。

主动式定位的椭球交会原理及其2种解法

空间球交会定位原理广泛应用,但并非所有主动式定位系统均适用该定位原理。该文探讨了主动式双程定位观测模型及与之适配的椭圆交会定位原理,分析了主动式定位模型化为空间交会定位模型的模型误差,提出了基于高斯-牛顿法的主动式定位模型直接求解算法,以消除参数初值误差及其模型非线性强度影响;同时,为了继承基于空间交会原理的定位模型和算法,提出了求解主动式定位模型的间接解法,该方法通过双程距离精确转化为单程距离实现。以主动式声呐定位应用场景为例,进行深海3 000 m海试数据验证,结果表明,主动式声呐定位模型相对于传统空间交会定位模型具有更高的定位精度,且直接解法与间接解法结果及其精度评定结果相同。其中,间接法通过建立往返双程距离比例关系,将模型近似误差进行等效转化,可对现有常规定位模型与算法直接加以利用,因此该方法具有更好的实用价值。

声呐渗流测试技术之质疑

介绍了近年来新出现的用声呐法测试渗流速度和渗流方向这一技术的核心内容,包括测试装置、测试原理和计算方法等。从理论和实践上对其提出了4方面的质疑:测试原理的理论基础、渗流速度计算方法、渗流方向确定和流场畸变。通过理论分析、公式的推导和数据的可靠性分析后认为:声呐测试渗流的原理缺乏理论解释;计算渗流速度的公式反映的只是孔内垂向流速度,实际还是由测量误差产生的伪垂向流速度,且该公式本身就推导不成立;无论垂向流是向上流动或向下流动,探头上部的水声器发射的声波到达下部平面对称分布的6个水声器的时间在理论上是相等的,因而也无法通过合成法求得渗流方向,所提供的渗流方向实际是由指数级时间误差计算得到的伪流向;即声呐法无法获得渗流速度,包括水平向渗流速度和垂直向流动速度,也无法获得水平向渗流方向。

利用前视和测高声呐的UUV地形跟踪动态路径生成方法

保持对海底地形的定高跟踪航行是无人水下航行器(UUV)执行海洋勘测和水下目标搜索任务时常采用的一种运动形式,其核心是UUV如何对未知起伏的海底地形进行实时探测,并基于探测信息在线、动态地生成跟踪路径,以实现对地形的定高跟踪航行,同时避免与地形发生碰撞。针对上述问题,提出了一种基于前视声呐探测地形信息、基于多项式拟合动态生成跟踪路径的方法。首先,UUV利用前视声呐对海底地形进行实时探测,对获得的地形探测数据进行仿射处理后,得到具有离散特性的定高仿射数据。然后,采用基于最小二乘准则的三次多项式方法对仿射数据进行拟合,生成基于多项式函数描述的UUV地形跟踪航行路径。最后,设计了一种包含声呐探测、数据仿射、路径生成和跟踪控制的动态执行框架,实现UUV的实时地形跟踪航行任务。文中所提出的跟踪路径生成和动态执行框架通过对典型的海底“上坡”地形和“山地”地形跟踪的仿真验证,证明了其有效性和可行性。

点云投影结合轻量化卷积神经网络实现三维成像声呐快速目标分类

三维成像声呐的成像结果是三维点云,基于点云的三维成像声呐目标分类方法具有网络结构复杂、计算量大的特点。针对这一问题提出了一种将三维成像声呐成像结果从三维点云投影至二维图像的方法,并且使用轻量化卷积神经网络实现了三维成像声呐快速目标分类。该方法首先对三维成像声呐波束形成后的波束域数据进行最大值滤波和阈值滤波,以降低点云数据维度;接着,依据三维成像声呐的波束方向,将点云投影为深度图和强度图,分别保存点云的位置信息和强度信息;然后,利用深度图和强度图分别作为第一个通道和第二个通道构建混合通道图,将混合通道图作为目标分类网络的输入,从而将三维点云的目标分类问题转换为二维图像的目标分类问题;最后使用MobileNetV2网络实现了三维成像声呐快速目标分类。实验结果表明,提出的投影方法可以用二维图像分类网络完成三维成像声呐点云的目标分类任务;而且训练过程中混合通道图比单独的深度图和强度图收敛速度更快,结合目标分类网络可以实时进行目标分类,在真实数据集上分类正确率达到了91.13%。

基于视觉显著性的主动声呐背景均衡方法

针对主动声呐检测输出中的混响与辐射噪声泄漏等干扰抑制问题,提出了一种基于视觉显著性检测的背景均衡方法。首先,建立起主动声呐图像的概念,从视觉显著性统计规律上研讨其与自然图像的相似性;然后,在图像处理框架下提出一种基于二维频谱残差显著性检测的主动声呐背景均衡新方法,并引入图像熵作为均衡效果的评价依据。经海试数据验证,该方法能有效抑制混响片状干扰与辐射噪声条带状干扰,增强回波亮点,相较于常规的加权二维均值背景均衡方法图像熵值降低约2/3,运算速度提高约一个数量级。

BELLHOP3D模型的多核并行及性能评估

通过三维射线模型BELLHOP3D能够计算海洋中的三维声场,在声呐性能预报以及海洋环境参数反演等应用中具有重要意义。然而随着计算海域范围的增大以及计算网格点分辨率的提高,水声传播对计算的需求也逐渐增大。文章面向天河2号超级计算平台的多核体系结构,针对BELLHOP3D计算特征,充分挖掘其中的并行性,采用OpenMP多核并行技术,实现BELLHOP3D的多核并行。最后,通过Munk波导算例以及三维楔形海底算例对射线模型的并行计算效率进行了测试,当计算节点中的24个计算核心全部用于计算时,两个算例的并行计算效率分别达到了83.3%和87.5%。

基于谱残差法的主动声呐疑似目标自动拾取

针对主动声呐干扰遍布的B显画面上目标亮点难以拾取的问题,本文提出一种基于谱残差法视觉显著性处理的疑似目标自动拾取方法。在主动声呐图像的概念之下,研讨了自然图像中视觉显著体的发现与主动声呐图像中疑似目标拾取的相似机理,依此进而说明了本文方法的机理、核心框架以及关键处理环节,包括预处理声呐图像、生成显著图、估算拾取阈值以及标定疑似目标等。海试数据处理结果表明:相较于基于局部相对能量分析的对照方法,本文方法对已知目标具有更低的漏报率,同时亮点拾取数量减少约40%,且处理耗时减少约37%。

结合偏航校正的合成孔径声呐大测绘带运动补偿方法

传统基于频域补偿相位的运动补偿方法难以校正姿态误差造成的波束照射方向偏转,因此提出了一种基于偏航校正的合成孔径声呐大测绘带运动补偿方法。首先,利用时延的距离空变特性校正时延估计野值,提高估计精度和稳健性;其次,通过在后向投影算法的成像过程中对实孔径成像网格进行旋转来校正偏航造成的波束照射方向偏转;最后,通过分段二维轨迹估计和分段成像克服运动误差的距离空变特性,实现大测绘带运动补偿。仿真结果表明,所提方法能够有效抑制偏航误差导致的点目标图像主瓣展宽、旁瓣升高和模糊现象。实验结果表明,相比于参考方法,所提方法在小偏航角和大偏航角条件下均显著提高了成像质量,有效校正了偏航造成的运动误差,实现了大测绘带精确成像。

多波束测深声呐自主数据质量监测及门限控制

为满足水面无人艇等海洋无人平台的需求,确保多波束测深声呐系统在无人监控和参数调节下具备可靠自主工作能力,本文提出一种数据质量在线监测及门限参数控制方法。通过建立基于质量和异常因子的监测模型,结合粒子滤波技术,提高海底地形跟踪门限控制抗差性。仿真和湖上试验显示该方法能判定数据有效性、适应地形变化,并提供合理深度门限,适用于多波束测深声呐系统的在线数据质量监测和无人自主控制。