为突破作业环境、劳动力等因素对渔业发展的制约,解决当前水下捕捞能见度低、机器人识别分辨率低等问题,促进渔业向现代化转型升级,从学术角度梳理、分析国内外水下机器人视觉系统关键技术发展趋势,为相关研究提供参考.基于 CiteSpace 软件,分别选取中国知网(China National Knowledge Infrastucture,CNKI)的数据库和 Web of Science的核心数据库的文献样本,从发文量分布、关键词共现图谱、关键词时间线图谱等方面完成可视化分析,并对水下捕捞机器人作业环境、视觉系统技术进行阐述.发文量和关键词时间线图谱表明,国内外水下机器人视觉系统关键技术的研发均经历初始阶段、探索阶段和成长阶段.关键词共现图谱分析表明,人机交互、识别算法设计等方面是水下机器人视觉系统相关研究的热点.未来,水下捕捞机器人视觉系统发展应聚焦智能化、自主化,重点解决目标检测优化、水生物数据库建立、数字化系统构建等多方面难点问题.
Development Trend Analysis of Underwater Fishing Robot Vision System
In order to break through the constraints of operation environment,labor force and other factors on the fishery development,solve the current problems of low visibility in underwater fishing and low resolution of robot identification,and promote the transformation and upgrading of the fishery toward the modernization,the development trend of key technologies of underwater robot vision system at home and abroad is sorted out and analyzed in an academic perspective to provide reference for the related research.Based on the CiteSpace software,the literature samples from the databases of China National Knowledge Infrastructure(CNKI)and core databases of Web of Science are selected respectively,the visualization analysis in quantity of published articles,keyword co-occurrence map,keyword timeline map,etc.,is completed,and the operation environment and vision system technology of underwater fishing robot are expounded.The quantity of published articles and keyword timeline map show that the research and development of key technologies of underwater robot vision system at home and abroad both go through the initial stage,exploration stage and growth stage.The keyword co-occurrence map shows that the human-computer interaction,recognition algorithm design and other aspects are the hot spots of relative research on the underwater robot vision system.In the future,the development of underwater fishing robot vision system should focus on the intelligence and autonomy,and the special stress should be laid on solving many difficult problems such as object detection optimization,aquatic organism database establishment,and digital system construction.
NETL
NSTL
万方数据
