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基于形状与纹理特征的鱼类摄食状态检测方法
引用本文:郭强,杨信廷,周超,吝凯,孙传恒,陈明.基于形状与纹理特征的鱼类摄食状态检测方法[J].上海海洋大学学报,2018,27(2):181-189.
作者姓名:郭强  杨信廷  周超  吝凯  孙传恒  陈明
作者单位:上海海洋大学信息学院;北京农业信息技术研究中心;国家农业信息化工程技术研究中心;
基金项目:国家重点研发计划(2017YFD0701705);北京市自然科学基金(6152009)
摘    要:在水产养殖中,检测鱼类的摄食状态对于投喂控制具有重要意义。以镜鲤为实验对象,提出了一种基于鱼群图像的形状及纹理特征和BP神经网络的鱼群摄食行为检测方法。首先,对采集到的图片进行背景减、灰度化、二值化等处理,得到图像形状与纹理信息,然后计算鱼群图像的形状参数和图像熵,最后利用BP神经网络建模,对鱼群的摄食状态进行检测识别。结果显示,本方法的正确识别率达到98.0%。与单一的基于纹理的检测方法相比,不仅可以把因水面抖动、水花等不利因素的干扰作为纹理的特有属性进行分析,而且考虑了图像的形状信息,提高了检测的准确性,可以用于指导水产养殖中的精准投喂控制。

关 键 词:摄食行为  形状和纹理特征  BP神经网络  精准投喂
收稿时间:2017/8/7 0:00:00
修稿时间:2017/11/16 0:00:00

Fish feeding behavior detection method based on shape and texture features
GUO Qiang,YANG Xinting,ZHOU Chao,LIN Kai,SUN Chuanheng and CHEN Ming.Fish feeding behavior detection method based on shape and texture features[J].Journal of Shanghai Ocean University,2018,27(2):181-189.
Authors:GUO Qiang  YANG Xinting  ZHOU Chao  LIN Kai  SUN Chuanheng and CHEN Ming
Affiliation:College of Information Science, Shanghai Ocean University, Shanghai 201306, China;Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China,College of Information Science, Shanghai Ocean University, Shanghai 201306, China;Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China,Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China,Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China,Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China and College of Information Science, Shanghai Ocean University, Shanghai 201306, China
Abstract:In the production practice, it is important to detect the feeding behavior of fish for feeding control. Taking Cyprinuscarpiospeculari as an experimental object, this paper employs computer vision technology to detect feeding behavior by using shape and texture information of fish in the process of feeding.Firstly, the image is subtracted, grayed out, binarized and so on, and the image shape and texture information are obtained. Then, the feeding behavior of fish is classified by BP neural network. Compared with the single texture-based detection method, this method can not only analyze the interference of the unfavorable factors such as surface vibration, spray,and other adverse factors, but also consider the shape information of the image and improve the accuracy of the detection. The results show that the correct recognition rate of this method is 98.0%, which can be used to guide the precise feeding control in aquaculture.
Keywords:feeding behavior  shape and texture features  BP neural network  precise feeding
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