基于机器视觉的水产养殖计数研究综述  

Review of research on aquaculture counting based on machine vision

作　　者：张涵钰 李振波[1,2,3,4] 李蔚然 杨普 ZHANG Hanyu;LI Zhenbo;LI Weiran;YANG Pu(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;National Innovation Center for Digital Fishery,Ministry of Agriculture and Rural Affairs,Beijing 100083,China;Key Laboratory of Smart Farming Technology,Ministry of Agriculture and Rural Affairs,Beijing 100083,China;Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,Beijing 100083,China)

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]农业农村部国家数字渔业创新中心,北京100083 [3]农业农村部智慧养殖技术重点实验室,北京100083 [4]农业农村部农业信息获取技术重点实验室,北京100083

出　　处：《计算机应用》2023年第9期2970-2982,共13页journal of Computer Applications

基　　金：国家重点研发计划项目(2020YFD0900204);广东省重点领域研发计划项目(2020B0202010009)。

摘　　要：养殖计数是水产养殖过程中的重要环节,计数结果为水产动物的饲料投喂、养殖密度调整和经济效益估算等方面提供重要依据。针对传统人工计数方法耗时费力且易造成较大误差的问题,大量基于机器视觉的方法与应用被提出,极大地推动了水产品无损计数的发展。为深入了解基于机器视觉的水产养殖计数研究,整理和分析了至今三十多年来国内外的相关文献。首先,从数据采集方面对水产养殖计数展开综述性介绍,并对机器视觉所需数据的获取方法进行概括;其次,从传统机器视觉和深度学习两方面对水产养殖计数方法进行分析与总结;然后,对各种计数方法在不同养殖环境的实际应用进行对比分析;最后,从数据、方法和应用三方面总结了水产养殖计数研究的发展难点,并提出了计数方法研究和装备应用的未来发展方向。Aquaculture counting is an important part of the aquaculture process,and the counting results provide an important basis for feeding,breeding density adjustment,and economic efficiency estimation of aquatic animals.In response to the traditional manual counting methods,which are time-consuming,labor-intensive,and prone to large errors,a large number of methods and applications based on machine vision have been proposed,thereby greatly promoting the development of non-destructive counting of aquatic products.In order to deeply understand the research on aquaculture counting based on machine vision,the relevant domestic and international literature in the past 30 years was collated and analyzed.Firstly,a review of aquaculture counting was presented in the perspective of data acquisition,and the methods for acquiring the data required for machine vision were summed up.Secondly,the aquaculture counting methods were analyzed and summarized in terms of traditional machine vision and deep learning.Thirdly,the practical applications of counting methods in different farming environments were compared and analyzed.Finally,the difficulties in the development of aquaculture counting research were summarized in terms of data,methods,and applications,and corresponding views were presented for the future trends of aquaculture counting research and equipment applications.

关 键 词：水产养殖 人工计数 无损计数 机器视觉 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]