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.

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.

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.

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.

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.

Motion estimation in wide band synthetic aperture sonar based on the raw echo data using the method of displaced phase center antenna

Phase errors in synthetic aperture sonar (SAS) imaging must be reduced to less than one eighth of a wavelength so as to avoid image destruction. Most of the phase errors occur as a result of platform motion errors, for example, sway yaw and surge that are the most important error sources. The phase error of a wide band synthetic aperture sonar is modeled and solutions to sway yaw and surge motion estimation based on the raw sonar echo data with a Displaced Phase Center Antenna (DPCA) method are proposed and their implementations are detailed in this paper. It is shown that the sway estimates can be obtained from the correlation lag and phase difference between the returns at coincident phase centers. An estimate of yaw is also possible if such a technique is applied to more than one overlapping phase center positions. Surge estimates can be obtained by identifying pairs of phase centers with a maximum correlation coefficient. The method works only if the platform velocity is low enough such that a number of phase centers from adjacent pings overlap.

High-resolution swath bathymetry using MIMO sonar system

For increasing the cross-track resolution, the multiple input multiple output （MIMO） technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIMO sonar is composed of two parallel transmitting uniform linear arrays （ULAs） and a receiving ULA which is perpendicular to the former. The spacing between the two transmitting ULAs is equal to the product of the receiving sensor number and the receiving inter-sensor spacing. Furthermore, two narrowband linear frequency modulation （LFM） pulses, sharing the same frequency band but with opposite modulation slopes, are used as transmitting waveforms of the two transmitting ULAs. With such an array layout and transmitting signals, the MIMO sonar can sound a swath with the cross-track resolution doubling that of the traditional multibeam sonar using a Mills cross array. Numerical examples are provided to verify the effectiveness of the proposed approach.

Waveform diversity based sonar system for target localization

A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed. Unlike the coherent signals between different elements in common active array, the transmitted signals from different elements here are spatially orthogonal waveforms which allow for array processing in the transit mode and result in an extension of array aperture. The mathematical derivation of Capon estimator for this sonar system is described in detail. And the performance of this orthogonal-waveform based sonar is an- alyzed and compared with that of its phased-array counterpart by water tank experiments. Experimental results show that this sonar system could achieve 12 dB-15 dB additional array gain over its phased-array counterpart, which means a doubling of maximum detection range. Moreover, the angular resolution is significantly improved at lower SNR.

Fast DOA estimation algorithm for MIMO sonar based on ant colony optimization

The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.

代码质量管理平台Sonar在软件项目管理中的应用研究

文中对代码质量管理平台Sonar的质量评估模型和技术优势进行了深入剖析和解读,介绍了将Sonar平台在软件项目管理中的意义和价值。从多个角度阐述了代码质量管理对软件产品质量的重要性,并通实际项目导入的数据比对分析展示了该平台对实现企业软件项目管理中的代码质量透明化的重大意义。

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.

Extraction of Morphometric Bedform Characteristics from Profiling Sonar Datasets Recorded in Shallow Coastal Waters of China

The morphological characteristics of small-scale bedforms were measured by means of an acoustic profiling sonar on the Dafeng tidal flat, Jiangsu, in 2009, and in the Jiulong Estuary, Xiamen, in 2010, respectively. The ＂multi-threshold value＂ method was utilized to reveal the morphological undulations along which bedforms were present. Analyses of the datasets obtained show that： （1） sand ripples can have irregular shapes, and （2） changes in bedform morphology are small within a single tidal cycle but may be significant over several tidal cycles. Fractal and variogram analyses of the seabed roughness revealed the existence of a significant relationship between current speed and the fractal dimension of the seabed roughness. As current speed increases, seabed roughness increases with a trend of smaller-scale bottom structures being replaced by larger-scale structures. Furthermore, the surface of the larger-scale bottom structures can either become smooth due to the absence of smaller-scale features or become rougher due to the presence of superimposed smaller-scale structures.

Sonar Image Detection Algorithm Based on Two-Phase Manifold Partner Clustering

According to the characteristics of sonar image data with manifold feature,the sonar image detection method based on two-phase manifold partner clustering algorithm is proposed. Firstly,K-means block clustering based on euclidean distance is proposed to reduce the data set. Mean value,standard deviation,and gray minimum value are considered as three features based on the relatinship between clustering model and data structure. Then K-means clustering algorithm based on manifold distance is utilized clustering again on the reduced data set to improve the detection efficiency. In K-means clustering algorithm based on manifold distance,line segment length on the manifold is analyzed,and a new power function line segment length is proposed to decrease the computational complexity. In order to quickly calculate the manifold distance,new allsource shortest path as the pretreatment of efficient algorithm is proposed. Based on this,the spatial feature of the image block is added in the three features to get the final precise partner clustering algorithm. The comparison with the other typical clustering algorithms demonstrates that the proposed algorithm gets good detection result. And it has better adaptability by experiments of the different real sonar images.

Response characteristics of sonar receiver under intense sound pulse

For anti-jamming and anti-countermeasure techniques of the sonar receiver, the response characteristics of the automatic gain control （AGC） circuit and the survivability of the prime circuit under strong interference are analyzed by simulations and experiments. An AGC simulation model based on the voltage control amplifier VCA810 prototype is proposed. Then static and dynamic simulations are realized with single frequency signal and linear frequency modulated （LFM） signal commonly used in the active sonar. Based on intense sound pulse （ISP） interference experiments, the real-time response characteristics of each module of the receiver are studied to verify the correctness of the model as well as the simulation results. Simulation and experiment results show that, under 252 dB/20 μs ISP interference, the specific sonar receiver will produce sustained cut top oscillation above 30 ms, which may affect the receiver and block the regular sonar signal.

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

基于SonarWiz的多波束声纳图像智能底质分类技术研究

多波束回波强度信息与海底底质类型具有较强的相关性,通过海底声纳图像能够实现底质类型的划分。为提高海底底质分类质量,依托SonarWiz的智能底质分类优势,在海底声纳图像纹理特征自动分类基础上,引入地形属性信息修正分类结果。以三亚崖州湾附近海域为例,基于实测海底地形数据和海底表面声纳图像,利用数据处理技术和图像分类方法,完成对该区域海底声纳图像处理分析和海底表层沉积物的分类研究,获得试验区海底底质分类成果。该方法对未知海底沉积物类型的大面积底质分类研究,以及港口、抛锚地等重点区域的底质分类工作具有借鉴意义。

Sonar Image Registration and Mosaic Based on Line Detection and Triangle Matching

Image registration is an important research topic in the field of computer vision,in which the registration and mosaic of side-scan sonar images is the keypoints of underwater navigation.However,the image registration method of keypoints is not suitable for sonar images which do not have obvious feature points.Therefore,a method of sonar-image registration and mosaic based on line segment extraction and triangle matching is proposed in this paper.Firstly,in order to extract features from sonar image,the LSD method is introduced to detect line feature from images,and line segments are filtered by the principle of attention;after that,triangles are formed from line segments,an image transformation matrix can be calculated through the heuristic greedy algorithm from these triangles;finally,images are merged based on the transformation information.On the basis of practical tests,it is found that,the feature extraction method used in this paper can better describe the outline of underwater terrain,and there is no obvious stitching gap between the result of sonar images stitched.Experimental results show that the proposed method is effective than the keypoints method of the registration and mosaic of sonar images.