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基于计算机视觉的鱼苗自动计数系统研究

  

  1. （1 衡阳师范学院物理与电子工程学院，湖南 衡阳 421000；
    2 宁波大学信息科学与工程学院，浙江 宁波 315211）
  • 出版日期:2016-06-20 发布日期:2022-09-15
  • 通讯作者: 徐建瑜（1973－），女，副教授，博士，研究方向：生物图像处理。E-mail: xujianyu@nbu.edu.cn
  • 作者简介:王文静（1988－），女，助教，研究方向：计算机视觉与图像处理。E-mail: 461621123@qq.com
  • 基金资助:
    浙江省重大科技攻关专项（2011C11049）；宁波市科技创新团队“海洋蟹类产业科技创新团队( 2011B81003)”；衡阳师范学院省级平台开放基金项目“基于机器视觉的生物幼苗数量估计（GD15K08）”；衡阳师范学院基金青年项目“基于虚拟仪器技术的室内环境监测系统的设计（14A05）”

Study on a computer vision based automatic counting system of fries

  1. (1 College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang 421000, China;
    2 Institute of Information Science and Technology, Ningbo University, Ningbo 315211, China)
  • Online:2016-06-20 Published:2022-09-15

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摘要： 为了在鱼苗的饲养、运输和销售过程中对一定数量或批量的幼苗进行精确计数，提出了一种基于计算机视觉的鱼苗自动计数系统。利用流体力学中伯努利原理（Bernoulli principle）设计了一个稳定流速的稳流水箱，使鱼苗和水一起以平稳恒定的速度流过过流计数箱体的拍摄区；使用电荷耦合元件（CCD）高速摄像头以与水流速度成比例的帧速采集图像，并传送给计算机进行图像处理；对图像进行阈值分割和目标提取后，计算出每帧图像中不重叠区域的幼苗数量，累加求得幼苗总量。结果表明，该系统计数的相对误差在15%以内，具有较高的精度。该研究不仅解决了目标粘连、连续计数和重复计数的问题，还可推广到虾苗、蟹苗等生物幼苗计数，具有通用性强、可行性好、推广范围大的特点。

关键词: 计算机视觉, 鱼苗自动计数, 稳流装置, 图像采集, 图像处理

Abstract: To realize the real-time, online, and accurate counting of a certain amount or quantities of seedlings  during farming, transportation and marketing, this research proposes an automatic counting system of fries based on computer vision. A tank device with steady flow velocity was designed according to Bernoulli principle in Fluid Mechanisms, enabling the seedlings and water to pass through the shooting area of the flow-through counting box more stably and constantly. Images were acquired using high speed CCD camera which had a frame rate consistent with the flow rate, and then transferred to a computer for image processing. The method of image threshold segmentation and target recognition was used, to work out the number of the seedlings that were non-overlapping in each frame image and then get the total number through adding up all the numbers. The experimental results showed that the relative error of the counting system was lower than 15%; in other words, it was of high precision. This counting system has solved the problems of target adhesion, continuous counting and double-counting, and could also be extended and applied in the seedling counting of shrimps, crabs and other biological organisms. In general, this method has the features of versatility, good feasibility and wide applicability.

Key words: computer vision, automatic counting of fries, steady flow, image capture, image processing