深水网箱养殖中的声学监测问题探讨

针对深水网箱养殖过程出现的一些诸如网衣安全、鱼类逃逸等问题,介绍了几种类型的监测技术应用:(1)声学警戒带方式构成的被动式网衣安全监测技术,监视声纳可以是单波束,也可以是机械扫描的多波束或电子扫描的多波束,同时采用水下机器人进行巡视;(2)基于网箱中鱼的目标强度,实现对网箱中养殖品种大小、数量的统计监测,鱼的目标强度主要取决于鱼的体态特征,同时也与发射声波的波长有关;(3)数量识别技术以及饵料投饲过程中的声学监测技术,通过将声纳输出信号反馈到投饵机,实现饵料投放自动化。     

网箱养殖智能投饲监控系统的设计

针对浑水海域网箱养殖中的投饲量控制问题,提出了一种采用回声探测方法进行智能投饲监控的系统原理及软硬件设计。在网箱底部安装一套收发合置的回声探测声纳,监测通过网箱饲料反射的声信号能量值,将其作为反馈量来调整饲料流量,使得过剩饲料维持在预设量,以实现智能饲量控制。为简化投饲监控系统并降低成本,可采取1套回声探测系统同时监控4个网箱的设计,并将各个监控节点组成WLAN后接入公网,以实现大型网箱养殖场的远程智能投饲监控。     

多波束深水网箱声学监测仪的研究

研究了一种声学监测设备,使网箱养殖者能在海水能见度较低时实现对鱼群的实时监测,进而掌握深水网箱中鱼群的生存状况。该声学监测仪由7个独立通道构成,能同时输出探测图像并通过监测仪的评估软件对数据进行分析。试验结果表明该设备能够对深水网箱中的生物量进行估计,可用于鱼类生长情况研究并监测鱼群的逃逸情况。     

侧扫声纳系统在石料人工鱼礁堆体积估算中的应用

采用侧扫声纳系统,对大连市金州湾蚂蚁岛海域中A、B两个石料人工鱼礁区进行调查评估,将声学方法应用到石料礁堆体积估算中,目的是探究石料礁堆体积估算的新方法,提高计算结果的准确性,进而提高工作效率。使用侧扫声纳系统进行数据采集,通过相关数据处理软件和计算机辅助技术对声纳数据进行后处理,提取石料礁堆特征图像,结合函数曲线对石料礁堆体积进行估算。结果显示,侧扫声纳图像能够清晰反映石料礁在海底的分布状态,根据图像能够对石料礁的冲淤程度进行判别,利用几何关系及相关技术手段对石料礁堆体积进行估算,估算结果存在一定误差,通过多次不同角度的检测或配合其他辅助软件能尽可能减小误差。在A石料礁区,运用积分断面法估算的石料礁堆体积与该礁区投放纪录的实际体积相比,相对误差接近16%;对B石料礁区进行检测评估,所得石料礁堆体积与实际体积之间的相对误差也接近16%。两个石料人工鱼礁区所估算的体积与实际体积相差均很少,虽然两个礁区物理环境以及石料礁堆大小均不同,但二者相对误差接近且均小于20%,验证了积分曲线的合理性。因此,在诸多不确定因素的影响下,该方法可相对精确地估算石料礁堆体积,可为人工鱼礁区建设、管理和评价提供支持和依据。     

水下声纳技术在国外渔业工程和其他方面的应用

在进行渔业工程(过鱼、拦鱼、导鱼)设计和水下渔业资源调查中，近年来国外已普遍使用水下声纳技术。水下声纳的原理是利用回声来量测水下物体的位置、大小、形状及运动方向。该系统的主要设备是回声探测仪，通过传感器发射不同频率、不同能量的声波，可以监控不同的范围。传感器是压电陶瓷制成的，有不同的截面积，可以固定在建筑物上，也可以安设在浮翼板上拖在船只后面；所接收的回声波，经计算机处理分析，得到预期的资料。下图为其原理示意图。     

鲤和鳙混养越冬对其成活率及新陈代谢的影响

本文定量地阐述了鲤和鳙混养越冬对其成活率及代谢的影响。工作完成于1983—1984年。秋季捕捞时当年鱼的密度为5.8万尾/ha。当年鱼越冬试验在网箱中进行。网箱设置于生产性越冬池出水口附近。箱内鱼的总生物量为6kg/m~3。据我们的资料(谢尔宾娜,1984),     

网箱存放商品鱼的防逃方法

网箱存放商品鱼的防逃方法为了将商品鱼（主要是鲤鱼）集中到旺季销售，北方养鱼户常提前把所养的鱼捕出，放置在设于空池中的大网箱中暂养，待鱼贩及单位、个人前来购买。由于在从网箱捞鱼过程中难免有鱼逃入空池中，也难免因操作不注意或捞鱼时踏破、刮破网箱而逃鱼，加...     

声纳及其在渔业上的应用

声纳(Sonar)是英语(Souod navigation and ranging)“声波导航和测距”缩写的音译。是指利用声的回波探测水下目标的各种技术参数。 1.声纳的主要形式声纳按工作方式分为主动声纳、被动声纳和通信声纳等三种。由声纳发射器向水中发射声信号,借助于目标反射来达到各种检测目的的声纳称为主动声纳,如图1所示。它发射一个具有一定型式的脉冲信号,经海水介质传播而到达目标,经目标反射后的回波信号(或称反射信号),又经海水介质传播而返回换能器,经收发转换而到达接收机。这类声纳多数用于搜索、定位和导航等方面。本身不发射声信号,而专门检测水下目标发出的声信号,来达到各种检测目的的声纳称为被动声     

网箱养鲤鱼病防治及给药方法

鱼病防治对网箱养鲤的养殖成败至关重要。本文结合作者近年来的网箱养殖科研成果和出口商品鱼的生产实际,明确了网箱养鲤的鱼病防治原则为防重于治、防治结合,中西结合、内服为主、外用为辅,阐述了网箱养鲤的选药、给药及防治方法,提供了网箱养鲤鱼病防治的中、西药组方和使用方法,认为采用"挂袋法"防治网箱养殖鱼病值得商榷,供网箱养殖者参考。     

网箱养鱼规模生产要抓好网箱育种

<正> 网箱育种是大水面开发网箱养鱼生产的重要配套环节。从夏花鱼苗至商品鱼出箱一直生活在同一环境中,其适应性强;沉箱越冬分箱后可提前开食,从而延长鱼种的生长期,提高商品鱼出箱规格。同时,对比由池塘引进鱼种入箱,网箱育种不需经拉网运输,可减少鱼体的机械损伤,防止鱼病引入,提高成活率。     

基于成像声呐的鱼类三维空间分布

针对海洋牧场中鱼群的三维空间分布问题,本研究提出一种利用成像声呐进行位置计算的方法。将成像声呐固定在船舷外侧的水下,并保证波束发射方向和声呐移动方向一致,通过走航的方式采集水下鱼群信息。首先对采集的原始数据进行图像处理,包括图像构建、背景去除、目标提取等,然后利用基于交互式多模型联合概率数据关联算法对水下目标进行关联处理,得到同一个目标在声呐水平视场中不同帧图像中的对应关系,在此基础上根据连续两帧图像中目标位置关系计算目标的空间坐标,最后结合关联算法获得多目标在三维空间中的运动轨迹以及深度分布情况。研究表明,本方法可以有效获取鱼群在水下的三维运动轨迹及其分布情况,这将为鱼类行为分析以及海洋牧场的资源评估提供技术支持。     

Hydroacoustic target strength validation using angling creel census data

Validation of hydroacoustic in-situ target strength is problematic in large, deep lowland rivers, which cannot be sampled easily by conventional methods such as netting or electric fishing. A sampling programme involving three different techniques (split beam sonar, angling census and post-angling competition data collection) was conducted to examine methodologies suitable for target strength validation. This combination of techniques also assessed the relative merits of each method for best describing fish populations and the stocks exploited in a recreational coarse fishery. The sonar estimated the greatest number of fish of the three techniques, with a strong positive size correlation with the other two methods. The angling census and post-competition census accounted for more larger fish, >26 cm, than were detected acoustically, indicating a stratification of species that were exploited by angling but not detected by horizontal sonar. The combined techniques demonstrated a suitable, cost-effective, hydroacoustic validation method for large UK rivers, which supports recreational coarse fisheries management, with the added advantage of species identification.     

A method to estimate the abundance of fish based on dual-frequency identification sonar (DIDSON) imaging

In this paper, a new method based on the dual-frequency identification sonar imaging is presented to estimate the abundance of fish. The proposed method can detect, track and count fish individually. Fish targets can be acquired with a depth-first search algorithm once its edge is detected based on the Sobel Operator from a complicated image. Multiple fish targets can be tracked using the nearest neighbor algorithm combined with the Extended Kalman Filtering. In order to validate the effectiveness of the proposed method, an experiment was conducted in Qingcaosha reservoir in Shanghai. The manual detection method based on sonar images was used as a reference method. The results showed that the fish were identified with less than 5% error compared with the manual detection results. The statistical error of fish numbers between the automated method and the reference method was approximately 10%.     

An evaluation of a steerable sidescan sonar for surveys of near-surface fish

A towed 330 kHz sector scanning sonar was evaluated for surveys of near-surface migratory salmonids (Oncorhynchus sp.) and other fish in the Strait of Georgia near the mouth of the Fraser River near Vancouver, BC (Canada). Detection of large near-surface fish presents a serious challenge to conventional net trawls or echo-sounder surveys due to vessel avoidance behaviour by the fish. The sonar was towed beside a research vessel on a custom-built towfish at a nominal depth of 18 m. The optimum configuration was determined to be with the sonar scanning a 30° vertical sector oriented perpendicular to the tow direction, at a ping rate of 5 Hz and a sector scan period of 5 s. Fish were detected up to a range of 100 m, limited by systemic noise and backscattered reverberation from the sea surface. Some evidence of vessel-avoidance behaviour by the fish was observed. Near surface temperature and salinity gradients are shown to induce downwards refraction of the sonar beam, resulting in biases in apparent depth and target strength of the fish. A simple model of the sonar performance in this scanning mode, including beam-pattern effects, is used to generate curves of fish target detectability and beam-pattern induced bias as a function of range and fish target strength. After correction for refraction effects, beam-pattern bias, and detectability, the survey results show reasonable agreement with net trawl and riverine escapement data.     

New applications of hydroacoustic methods for monitoring shallow water aquatic ecosystems: the case of mussel culture grounds

The development of acoustics tools and methods for monitoring anthropized ecosystems represents a new field for the application of acoustics. Monitoring such an environment was not possible with single vertical echo sounders, due to the fact that the artificial structures and the natural targets were not distinguishable. Monitoring data were collected along the French Mediterranean coastline, during five short surveys of mussel culture longline areas. Both the Reson Seabat 6012 multibeam sonar (455 kHz) and the Simrad SR 240 omnidirectional sonar (23.75 kHz) were used for target detection. The former tools allow accurate allocation of the different types of echoes to artefacts, fish schools and scattered fish. The school characteristics collected included morphological, geographical (GPS, school location), and behavioural (connections with the longlines). An acoustic survey undertaken with the same hardware near the study area allowed the comparison of fish schools and the TS distribution of individual fish in the open sea and in the mussel area. These data permitted us to evaluate the ecological impact of a mussel culture on the ecosystem, in a context of predation behaviour of fish on these longlines. Finally, the acoustic data revealed the configuration of each concession and the level of charge of each line. We discuss the applicability of this technology for in situ real time monitoring for joint management of such ecosystems. The information can allow littoral cooperative management or incorporating it into an ecosystem approach.     

Evaluation of a stimulus-response method for distinguishing out-migrant salmonids from drifting debris for sonar counts in the Trinity River,California

A method for discriminating fish from debris in sonar counts of out-migrant salmonids was tested in the Trinity River, California. The method used induced fish movements to distinguish fish from drifting debris. Electricity and light served as stimuli and video and split-beam sonar were used to measure movements of fish (mainly juvenile chinook salmon) and debris (mainly tree leaves). Differences in fish and debris behavior were clearly observable with underwater video. Many fish darted or slowed and most fish dove, whereas debris drifted passively. Fish responded to the electric field inconsistently, and an apparent positive phototaxis was the most consistent response to stimuli. Lack of matched sonar and video observations of individual targets prevented direct testing of sonar’s ability to differentiate fish and debris in the Trinity River. However, analysis of sonar data from a similar situation in the Seton River, British Columbia, indicate that fish responses measured in the Trinity River by video were within the resolution of split-beam sonar. Split-beam position measurement error averaged ≤ 0.06 m within 5° of the acoustic axis, compared to a mean diving reaction of 0.11 m by salmonids observed with video. Proper transducer deployment, improved sonar analysis methods, and perfection of stimuli to elicit obvious and consistent fish responses are key to the success of this technique. With suitable development and validation, the stimulus-response method could become a useful tool for apportioning sonar counts among fish and debris.     

Integration of sonar and optical camera images using deep neural network for fish monitoring

Fish monitoring in aquaculture farms is indispensable for managing the growth and health status of fish resources. However, it is unrealistic to expect humans to be able to perform monitoring at night, when standard optical cameras are generally inapplicable. Although sonar systems can be used at night, their practical applications are limited by their monochrome, low-quality images. In this paper, we describe a realistic image generation system that uses sonar and camera images recorded at night. The proposed approach is based on conditional generative adversarial networks, which learn the image-to-image translation between sonar and optical images. We tested the system in a fish tank containing thousands of sardines (Sardinops melanostictus). Images were simultaneously recorded using high-precision imaging sonar and an underwater camera. Experimental results show that the proposed model successfully generates realistic daytime images from sonar and night camera images. Our system enables nighttime monitoring using sonar and an optical camera, leading to more efficient fish farming and environmental surveillance.