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.

Underwater Gas Leakage Flow Detection and Classification Based on Multibeam Forward-Looking Sonar

The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.

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。

基于轻量化YOLOv7算法的侧扫声纳图像沉船检测

针对现有的侧扫声纳图像水下沉船检测方法存在检测速度慢,传统的YOLOv5算法存在的漏检的问题,提出基于轻量化YOLOv7算法的水下沉船检测改进方法。首先,通过随机翻转、随机噪声等操作扩充沉船图像的样本数量;然后,引入迁移学习策略,将在COCO数据集上学习到的权重迁移到沉船检测的YOLOv7网络中;其次,改进模型损失函数中惩罚项的计算方式,提升收敛速度;最后在YOLOv7网络中引入FasterNet结构,减少模型的参数量和计算复杂度,降低模型对硬件的需求,达到轻量化模型的目的。实验结果表明,改进方法较原始YOLOv7算法在类平均精度值(mAP值)上提升了4.75%,检测速度也由原来的0.0218秒/帧提升到0.0179秒/帧,证明了改进方法的工程应用价值。

多子阵SAS方位空变运动补偿子孔径算法

为解决方位空变的侧摆和偏航误差存在情形下合成孔径声纳的快速运动补偿与成像问题,提出一种多子阵合成孔径声纳方位空变运动补偿子孔径算法。首先,建立运动误差存在情形下的双程距离历程模型,并利用泰勒级数展开对双根号形式距离历程进行近似;然后,利用子孔径运动补偿和单基等效处理,将含有方位空变的侧摆和偏航误差的多子阵回波数据转换为理想的单阵回波数据;最后,利用经典的单阵频域逐线成像算法,实现快速运动补偿和高分辨成像。仿真实验与实测数据成像结果均验证了所提算法的有效性。

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。

基于计算机图形的声呐图像生成关键技术研究

为提高水下目标探测的图像质量,研究一种基于计算机图形的声呐图像生成(Computer Graphics-based Sonar Image Generation,CG-SIG)方法。该方法结合声学物理建模、计算机图形渲染、深度学习优化技术。实验结果表明,CG-SIG方法生成的声呐图像在峰值信噪比(Peak Signal to Noise Ratio,PSNR)、结构相似性(Structural Similarity,SSIM)、平均主观得分(Mean Opinion Score,MOS)指标上均优于传统方法,为水下目标探测提供了可靠的图像支持。

联合多波束与侧扫声纳的海缆检测方法

为解决海底电缆路由检测中多波束扫测分辨率低、侧扫声纳(SSS)图像位置不准且存在误识别问题,提出了一种联合多波束与侧扫声纳的海底电缆路由自动检测方法,利用多波束图像高精度的位置信息纠正侧扫声纳图像,并顾及侧扫声纳图像海缆成像特点进行线状特征增强与边缘提取,实现海底电缆目标的自动、准确检测。实验结果表明,该方法能实现非掩埋海缆探测的准确探测,为海底电缆检测提供了多源测量信息融合的新思路,丰富了海底电缆检测的技术方法。

结合Lee滤波的NL-Means声呐图像滤波方法

声呐图像中存在的散斑噪声不仅极大地影响了图像质量,还对图像的后续分割、增强、边缘检测等增加了很多负面影响.针对这一问题,提出了结合Lee滤波的NL-Means算法:先用Lee滤波算法对声呐图像进行一次滤波,再用滤波后的图像计算NL-Means算法中的权重部分的权值,最后用NL-Means算法结合该权重进行滤波.改进的滤波方法避免了权重计算过程中由于噪声的影响而使导致权重偏小的问题,精确了NL-Means算法中的权重分配.实验证明,改进方法对声呐图像的散斑噪声不仅有更好的抑制效果,还提升了图像的质量.

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。