基于计算机视觉的鱼苗自动计数系统研究
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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。
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 flowthrough 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 than15%; 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.
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 flowthrough 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 than15%; 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.
引文
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[16]陈一之.基于伯努利方程实验仪的流体力学综合实验仪研制[J].长沙大学学报:自科版,2005,2(1):26-27.
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[18]孙劲光,张丈斌,朱世安.图象灰度的处理方法及实现[J].辽宁工程技术大学学报:自然科学版,2002,21(3):340-341.
[19]薛同泽,崔博.基于计算机图像识别技术的坯布疵点检测研究[J].仪表技术与传感器,2008(6):109-112.
[20]王序哲.局部自适应二值化方法研究[J].软件导刊,2011,10(11):13-14.
[21]符翔,张剑,王维,等.一种新的局部阈值分割算法[J].计算机应用与软件,2015,32(4):195-196.
[22]庞静洁.基于视频的鱼类运动目标检测与跟踪方法研究[D].秦皇岛:燕山大学,2014.
[23]黄玲,胡波,曹乃文.基于图像处理的鱼苗计数方法[J].湖北农业科学,2012,51(9):1180-1182.
[24]王硕,范良忠,刘鹰,等.基于计算机视觉的大菱鲆鱼苗计数方法研究[J].渔业现代化,2015,42(1):16-19.
[25]CHATAIN B,DEBAS L,BOURDILLON A.A photographic larval fish counting technique:comparison with other methods,statistical appraisal of the procedure and practicaluse[J].Aquaculture,1996,141:83-96.
[26]TOH Y H,NG,T M,LIEW B K.Automated fish counting using image processing[C]//Computational Intelligence and Software Engineering,2009:1-5.
[27]徐建瑜,王文静,王春琳,等.基于计算机视频处理的鱼虾蟹苗自动计数装置及其方法:CN201210244520.8[P].2012-12-15.)
[2]卢建琦.称重式鱼(虾)苗计数方法及其设备:CN88100193.7[P].1989-8-9.
[3]徐建瑜,刘鹰,崔绍荣,等.计算机视觉技术在鱼类应激状态监测中应用研究[J].渔业现代化,2008,35(3):15-18.
[4]徐建瑜,崔绍荣,苗香雯,等.计算机视觉技术在水产养殖中的应用与展望[J].农业工程学报,2005,21(8):174-178.
[5]徐愫,田云臣,马国强,等.计算机视觉在水产养殖与生产领域的应用[J].天津农学院学报,2014,21(4):43-46.
[6]GM爦B,BALABAN M,NLSAYM M.Machine vision applications to aquatic foods:A review[J].Turkish Journal of Fisheries and Aquatic Sciences,2011(11):171-181.
[7]GOMES J F S,LETA F R.Applications of computer vision techniques in the agriculture and food industry:a review[J].European Food Research&Technology,2012,235(6):989-1000.
[8]范嵩,刘娇,杨轶.图像识别技术在鱼苗计数方面的研究与实现[J].水产科学,2008,27(4):210-212.
[9]FOSTER M,PETRELL R,ITO M R,et al.Detection and counting of uneaten food pellets in a sea cage using image analysis[J].Aquacultural Engineering,1995,4(3):251-269.
[10]FAN L Z,LIU Y.Automate fry counting using computer vision and multi-class least squares support vector machine[J].Aquaculture,2013,380-383:91-98.
[11]NEWBURY P F,CULVERHOUSE P F,PILGRIM D A.Automatic fish population counting by artificial neural network[J].Aquaculture,1995,133(1):45-55.
[12]刘世晶,陈军,刘兴国,等.基于图像处理技术的小球藻荧光图像自动计数方法研究[J].渔业现代化,2012,39(5):16-19.
[13]朱从容.一种基于机器视觉的鱼苗自动计数方法[J].渔业现代化,2009,36(2):25-28.
[14]朱从容.基于计算机视觉的鱼苗自动计数系统:CN200810162176.1[P].2009-5-13.
[15]赵毅山.流体力学[M].上海:同济大学出版社,2004:69-87.
[16]陈一之.基于伯努利方程实验仪的流体力学综合实验仪研制[J].长沙大学学报:自科版,2005,2(1):26-27.
[17]梁智权.流体力学[M].重庆:重庆大学出版社,2002:101-103.
[18]孙劲光,张丈斌,朱世安.图象灰度的处理方法及实现[J].辽宁工程技术大学学报:自然科学版,2002,21(3):340-341.
[19]薛同泽,崔博.基于计算机图像识别技术的坯布疵点检测研究[J].仪表技术与传感器,2008(6):109-112.
[20]王序哲.局部自适应二值化方法研究[J].软件导刊,2011,10(11):13-14.
[21]符翔,张剑,王维,等.一种新的局部阈值分割算法[J].计算机应用与软件,2015,32(4):195-196.
[22]庞静洁.基于视频的鱼类运动目标检测与跟踪方法研究[D].秦皇岛:燕山大学,2014.
[23]黄玲,胡波,曹乃文.基于图像处理的鱼苗计数方法[J].湖北农业科学,2012,51(9):1180-1182.
[24]王硕,范良忠,刘鹰,等.基于计算机视觉的大菱鲆鱼苗计数方法研究[J].渔业现代化,2015,42(1):16-19.
[25]CHATAIN B,DEBAS L,BOURDILLON A.A photographic larval fish counting technique:comparison with other methods,statistical appraisal of the procedure and practicaluse[J].Aquaculture,1996,141:83-96.
[26]TOH Y H,NG,T M,LIEW B K.Automated fish counting using image processing[C]//Computational Intelligence and Software Engineering,2009:1-5.
[27]徐建瑜,王文静,王春琳,等.基于计算机视频处理的鱼虾蟹苗自动计数装置及其方法:CN201210244520.8[P].2012-12-15.)