基于光流法与图像纹理特征的鱼群摄食行为检测
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摘要
【目的】借助计算机视觉技术检测鱼群的摄食行为变化,为实现水产养殖中的精准投喂提供参考。【方法】以彩鲤为试验对象,首先对鱼群摄食过程的图像进行中值滤波、直方图均衡化预处理,提取目标鱼群前景图像,然后通过灰度共生矩阵计算鱼群的纹理特征,利用Lucas-Kanade光流法计算鱼群的运动方向矢量,并采用方向熵表征鱼群运动的混乱程度,最后利用支持向量机算法对鱼群摄食图像进行训练检测。【结果】图像预处理方法减少了水质浑浊对鱼群图像检测的影响;基于光流法与图像纹理特征定量分析鱼群的摄食过程变化,避免了水面抖动及水花等复杂因素的影响。该方法的检测准确率达97.0%,基本上能检测识别出所有的鱼群摄食状态与正常游动状态图像;与基于形状和纹理特征的检测方法相比,其检测精度提高4.5%(绝对值),可更好地满足池塘养殖环境下的鱼群摄食行为检测工作需要。【建议】在今后的研究中将获取更多鱼群在不同环境下的摄食图像,以提高模型泛化性,同时对鱼群不同摄食阶段特征参数设定阈值告警,以便更好地应用于智能饵料投喂设备研究。
【Objective】Using computer vision technology to detect changes in feeding behavior of shoal could provide a reference for accurate feeding in aquaculture.【Method】Taking Cyprinus carpio var. color as an experimental object,median filtering and histogram equalization pre-processing were used to extract the foreground image of the target shoal,and the texture features of the shoal were calculated by the gray level co-occurrence matrix. The direction vector of the shoal was calculated by Lucas-Kanade optical flow method,then used direction entropy to characterize the chaos of shoal movement. Finally,the support vector machine algorithm was used to train and test the shoal feeding image.【Result】In this paper,the image preprocessing method was used to reduce the influence of water turbidity on fish image detection.The optical flow method and image texture features quantitatively analyzed the changes in the feeding process of the shoal,avoiding the influence of complex factors such as water surface jitter and water splash. The experimental results showed that the detection accuracy of this method reached 97.0%,and it could basically detect the images of all the shoal feeding state and normal swimming state. Compared with the detection methods based on shape and texture features,the detection accuracy was improved by 4.5%(absolute value),this method could better solve the problem of shoal feeding behavior detection in pond culture environment.【Suggestion】In the subsequent study,more shoal feeding image in different environments will be used to improve the generalization of the model. In addition,threshold alarms will be set for the features parameters of different feeding stages of the fish to better apply to the research of intelligent bait feeding equipment.
【Objective】Using computer vision technology to detect changes in feeding behavior of shoal could provide a reference for accurate feeding in aquaculture.【Method】Taking Cyprinus carpio var. color as an experimental object,median filtering and histogram equalization pre-processing were used to extract the foreground image of the target shoal,and the texture features of the shoal were calculated by the gray level co-occurrence matrix. The direction vector of the shoal was calculated by Lucas-Kanade optical flow method,then used direction entropy to characterize the chaos of shoal movement. Finally,the support vector machine algorithm was used to train and test the shoal feeding image.【Result】In this paper,the image preprocessing method was used to reduce the influence of water turbidity on fish image detection.The optical flow method and image texture features quantitatively analyzed the changes in the feeding process of the shoal,avoiding the influence of complex factors such as water surface jitter and water splash. The experimental results showed that the detection accuracy of this method reached 97.0%,and it could basically detect the images of all the shoal feeding state and normal swimming state. Compared with the detection methods based on shape and texture features,the detection accuracy was improved by 4.5%(absolute value),this method could better solve the problem of shoal feeding behavior detection in pond culture environment.【Suggestion】In the subsequent study,more shoal feeding image in different environments will be used to improve the generalization of the model. In addition,threshold alarms will be set for the features parameters of different feeding stages of the fish to better apply to the research of intelligent bait feeding equipment.
引文
陈彩文,杜永贵,周超,孙传恒.2017.基于图像纹理特征的养殖鱼群摄食活动强度评估[J].农业工程学报,33(5):232-237.[Chen C W,Du Y G,Zhou C,Sun C H.2017.Evaluation of feeding activity of shoal based on image texture[J].Transactions of the Chinese Society of Agricultural Engineering,33(5):232-237.]
陈彩文,杜永贵,周超,孙传恒.2018.基于支持向量机的鱼群摄食行为识别技术[J].江苏农业科学,46(7):226-229.[Chen C W,Du Y G,Zhou C,Sun C H.2018.Study on fish feeding behavior recognition technology based on support vector machine[J].Jiangsu Agricultural Sciences,46(7):226-229.]
杜鉴豪,许力.2011.基于区域光流特征的异常行为检测[J].浙江大学学报(工学版),45(7):1161-1166.[Du J H,Xu L.2011.Abnormal behavior detection based on regional optical flow[J].Journal of Zhejiang University(Engineering Science),45(7):1161-1166.]
段延娥,李道亮,李振波,傅泽田.2015.基于计算机视觉的水产动物视觉特征测量研究综述[J].农业工程学报,31(15):1-11.[Duan Y E,Li D L,Li Z B,Fu Z T.2015.Review on visual attributes measurement research of aquatic animals based on computer vision[J].Transactions of the Chinese Society of Agricultural Engineering,31(15):1-11.]
郭强,杨信廷,周超,吝凯,孙传恒,陈明.2018.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,27(2):181-189.[Guo Q,Yang X T,Zhou C,Lin K,Sun C H,Chen M.2018.Fish feeding behavior detection method based on shape and texture features[J].Journal of Shanghai Ocean University,27(2):181-189.]
胡金有,王靖杰,张小栓,傅泽田.2015.水产养殖信息化关键技术研究现状与趋势[J].农业机械学报,46(7):251-263.[Hu J Y,Wang J J,Zhang X S,Fu Z T.2015.Research status and development trends of information technologies in aquacultures[J].Transactions of the Chinese Society for Agricultural Machinery,46(7):251-263.]
刘丽,匡纲要.2009.图像纹理特征提取方法综述[J].中国图象图形学报,14(4):622-635.[Liu L,Kuang G Y.2009.Overview of image textural feature extraction methods[J].Journal of Image and Graphics,14(4):622-635.]
乔峰,郑堤,胡利永,魏玉艳.2015.基于机器视觉实时决策的智能投饵系统研究[J].工程设计学报,22(6):528-533.[Qiao F,Zheng D,Hu L Y,Wei Y Y.2015.Research on smart bait casting machine based on machine vision technology[J].Chinese Journal of Engineering Design,22(6):528-533.]
王勇平,聂余满,谢成军,双丰.2015.基于机器视觉的养殖鱼群智能投饵系统设计与研究[J].仪表技术,(1):1-4.[Wang Y P,Nie Y M,Xie C J,Shuang F.2015.Design and research of intelligent feeding system for farmed fish based on machine vision[J].Instrumentation Technology,(1):1-4.]
邢克智,郭永军,陈成勋,田云臣,华旭峰,徐大为.2013.水产养殖先进传感与智能处理关键技术及产品[J].天津科技,(2):45-46.[Xing K Z,Guo Y J,Chen C X,Tian Y C,Hua X F,Xu D W.2013.Key technologies and products for advanced sensing and intelligent processing in aquaculture[J].Tianjin Science&Technology,(2):45-46.]
于欣,侯晓娇,卢焕达,余心杰,范良忠,刘鹰.2014.基于光流法与特征统计的鱼群异常行为检测[J].农业工程学报,30(2):162-168.[Yu X,Hou X J,Lu H D,Yu X J,Fan LZ,Liu Y.2014.Anomaly detection of fish school behavior based on features statistical and optical flow methods[J].Transactions of the Chinese Society of Agricultural Engineering,30(2):162-168.]
张学工.2000.关于统计学习理论与支持向量机[J].自动化学报,26(1):32-42.[Zhang X G.2000.Introduction to statistical learning theory and support vector machines[J].Acta Automatica Sinica,26(1):32-42.]
赵建,朱松明,叶章颖,刘鹰,李勇,卢焕达.2016.循环水养殖游泳型鱼类摄食活动强度评估方法研究[J].农业机械学报,47(8):288-293.[Zhao J,Zhu S M,Ye Z Y,Liu Y,Li Y,Lu H D.2016.Assessing method for feeding activity of swimming fishes in RAS[J].Transactions of the Chinese Society for Agricultural Machinery,47(8):288-293.]
周应祺,王军,钱卫国,曹道梅,张仲秋,柳玲飞.2013.鱼类集群行为的研究进展[J].上海海洋大学学报,22(5):734-743.[Zhou Y Q,Wang J,Qian W G,Cao D M,Zhang ZQ,Liu L F.2013.Review of fish schooling behavior study[J].Journal of Shanghai Ocean University,22(5):734-743.]
Andrade E L,Blunsden S,Fisher R B.2005.Characterisation of optical flow anomalies in pedestrian traffic[C]//The IEE International Symposium on Imaging for Crime Prevention and Detection.The IEE International Symposium on:73-78.
Delcourt J,Denoel M,Yileff M,Poncin P.2013.Video multitracking of fish behavior of fish behavior:A synthesis and future perspectives[J].Fish and Fisheries,14(2):186-204.
Delphine M,Dominique P.2014.Underwater video techniques for observing coastal marine biodiversity:A review of sixty years of publications(1952-2012)[J].Fisheries Research,154:44-62.
Hu L Y,Zheng D,Qiao F,Zhao J M,Chen J H.2016.Research on smart cast for fish cage aquaculture based on image processing[C]//Proceedings of 2016 2ndWorkshop on Advanced Research and Technology in Industry Applications(WARTIA 2016).Computer Science and Electronic Technology International Society:6.
Liu Z Y,Li X,Fan L Z,Lu H D,Liu L,Liu Y.2014.Measuring feeding activity of fish in RAS using computer vision[J].Aquacultural Engineering,60:20-27.
Lucas B D,Kanade T.1981.An iterative image registration technique with an application to stereo vision[C]//Proceedings of the 1981 DARPA Imaging Understanding Workshop.Carnegie Mellon University,the Robotics Institute:121-130.
Ma H,Tsai T F,Liu C C.2010.Real-time monitoring of water quality using temporal trajectory of live fish[J].Expert Systems with Applications,37(7):5158-5171.
Pautsina A,Císa?P,?tys D,Terjesen B F,Espmark?M O.2015.Infrared reflection system for indoor 3D tracking of fish[J].Aquacultural Engineering,69:7-17.
Sadoul B,Mengues P E,Friggens N C,Prunet P,Colson V.2014.A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions[J].Aquaculture,430:179-187.
Zhao J,Gu Z B,Shi M M,Lu H D,Li J P,Shen M W,Ye ZY,Zhu S M.2016.Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system[J].Computers and Electronics in Agriculture,127:271-280.
陈彩文,杜永贵,周超,孙传恒.2018.基于支持向量机的鱼群摄食行为识别技术[J].江苏农业科学,46(7):226-229.[Chen C W,Du Y G,Zhou C,Sun C H.2018.Study on fish feeding behavior recognition technology based on support vector machine[J].Jiangsu Agricultural Sciences,46(7):226-229.]
杜鉴豪,许力.2011.基于区域光流特征的异常行为检测[J].浙江大学学报(工学版),45(7):1161-1166.[Du J H,Xu L.2011.Abnormal behavior detection based on regional optical flow[J].Journal of Zhejiang University(Engineering Science),45(7):1161-1166.]
段延娥,李道亮,李振波,傅泽田.2015.基于计算机视觉的水产动物视觉特征测量研究综述[J].农业工程学报,31(15):1-11.[Duan Y E,Li D L,Li Z B,Fu Z T.2015.Review on visual attributes measurement research of aquatic animals based on computer vision[J].Transactions of the Chinese Society of Agricultural Engineering,31(15):1-11.]
郭强,杨信廷,周超,吝凯,孙传恒,陈明.2018.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,27(2):181-189.[Guo Q,Yang X T,Zhou C,Lin K,Sun C H,Chen M.2018.Fish feeding behavior detection method based on shape and texture features[J].Journal of Shanghai Ocean University,27(2):181-189.]
胡金有,王靖杰,张小栓,傅泽田.2015.水产养殖信息化关键技术研究现状与趋势[J].农业机械学报,46(7):251-263.[Hu J Y,Wang J J,Zhang X S,Fu Z T.2015.Research status and development trends of information technologies in aquacultures[J].Transactions of the Chinese Society for Agricultural Machinery,46(7):251-263.]
刘丽,匡纲要.2009.图像纹理特征提取方法综述[J].中国图象图形学报,14(4):622-635.[Liu L,Kuang G Y.2009.Overview of image textural feature extraction methods[J].Journal of Image and Graphics,14(4):622-635.]
乔峰,郑堤,胡利永,魏玉艳.2015.基于机器视觉实时决策的智能投饵系统研究[J].工程设计学报,22(6):528-533.[Qiao F,Zheng D,Hu L Y,Wei Y Y.2015.Research on smart bait casting machine based on machine vision technology[J].Chinese Journal of Engineering Design,22(6):528-533.]
王勇平,聂余满,谢成军,双丰.2015.基于机器视觉的养殖鱼群智能投饵系统设计与研究[J].仪表技术,(1):1-4.[Wang Y P,Nie Y M,Xie C J,Shuang F.2015.Design and research of intelligent feeding system for farmed fish based on machine vision[J].Instrumentation Technology,(1):1-4.]
邢克智,郭永军,陈成勋,田云臣,华旭峰,徐大为.2013.水产养殖先进传感与智能处理关键技术及产品[J].天津科技,(2):45-46.[Xing K Z,Guo Y J,Chen C X,Tian Y C,Hua X F,Xu D W.2013.Key technologies and products for advanced sensing and intelligent processing in aquaculture[J].Tianjin Science&Technology,(2):45-46.]
于欣,侯晓娇,卢焕达,余心杰,范良忠,刘鹰.2014.基于光流法与特征统计的鱼群异常行为检测[J].农业工程学报,30(2):162-168.[Yu X,Hou X J,Lu H D,Yu X J,Fan LZ,Liu Y.2014.Anomaly detection of fish school behavior based on features statistical and optical flow methods[J].Transactions of the Chinese Society of Agricultural Engineering,30(2):162-168.]
张学工.2000.关于统计学习理论与支持向量机[J].自动化学报,26(1):32-42.[Zhang X G.2000.Introduction to statistical learning theory and support vector machines[J].Acta Automatica Sinica,26(1):32-42.]
赵建,朱松明,叶章颖,刘鹰,李勇,卢焕达.2016.循环水养殖游泳型鱼类摄食活动强度评估方法研究[J].农业机械学报,47(8):288-293.[Zhao J,Zhu S M,Ye Z Y,Liu Y,Li Y,Lu H D.2016.Assessing method for feeding activity of swimming fishes in RAS[J].Transactions of the Chinese Society for Agricultural Machinery,47(8):288-293.]
周应祺,王军,钱卫国,曹道梅,张仲秋,柳玲飞.2013.鱼类集群行为的研究进展[J].上海海洋大学学报,22(5):734-743.[Zhou Y Q,Wang J,Qian W G,Cao D M,Zhang ZQ,Liu L F.2013.Review of fish schooling behavior study[J].Journal of Shanghai Ocean University,22(5):734-743.]
Andrade E L,Blunsden S,Fisher R B.2005.Characterisation of optical flow anomalies in pedestrian traffic[C]//The IEE International Symposium on Imaging for Crime Prevention and Detection.The IEE International Symposium on:73-78.
Delcourt J,Denoel M,Yileff M,Poncin P.2013.Video multitracking of fish behavior of fish behavior:A synthesis and future perspectives[J].Fish and Fisheries,14(2):186-204.
Delphine M,Dominique P.2014.Underwater video techniques for observing coastal marine biodiversity:A review of sixty years of publications(1952-2012)[J].Fisheries Research,154:44-62.
Hu L Y,Zheng D,Qiao F,Zhao J M,Chen J H.2016.Research on smart cast for fish cage aquaculture based on image processing[C]//Proceedings of 2016 2ndWorkshop on Advanced Research and Technology in Industry Applications(WARTIA 2016).Computer Science and Electronic Technology International Society:6.
Liu Z Y,Li X,Fan L Z,Lu H D,Liu L,Liu Y.2014.Measuring feeding activity of fish in RAS using computer vision[J].Aquacultural Engineering,60:20-27.
Lucas B D,Kanade T.1981.An iterative image registration technique with an application to stereo vision[C]//Proceedings of the 1981 DARPA Imaging Understanding Workshop.Carnegie Mellon University,the Robotics Institute:121-130.
Ma H,Tsai T F,Liu C C.2010.Real-time monitoring of water quality using temporal trajectory of live fish[J].Expert Systems with Applications,37(7):5158-5171.
Pautsina A,Císa?P,?tys D,Terjesen B F,Espmark?M O.2015.Infrared reflection system for indoor 3D tracking of fish[J].Aquacultural Engineering,69:7-17.
Sadoul B,Mengues P E,Friggens N C,Prunet P,Colson V.2014.A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions[J].Aquaculture,430:179-187.
Zhao J,Gu Z B,Shi M M,Lu H D,Li J P,Shen M W,Ye ZY,Zhu S M.2016.Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system[J].Computers and Electronics in Agriculture,127:271-280.
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