基于机器视觉的脐橙采后田间分级系统设计
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摘要
为了满足广大果农对采后脐橙及时分级的需求,设计一套基于机器视觉的脐橙采后田间分级系统,由输送系统、视觉系统和分拣系统组成.该系统能检测脐橙的大小、表面缺陷数量及缺陷面积,并根据分级标准对水果进行自动分级.试验结果表明,单幅图片的平均检测时间小于30 ms,尺寸的检测误差小于3%,缺陷检测率为99%,缺陷面积检测误差小于7%.与传统检测系统相比,该系统检测速度快,结构紧凑,适合脐橙的田间分级检验.
To meet the demand of fruit farmers' timely grading of navel oranges,a set of navel orange postharvest field grading system was designed based on machine vision,which was composed of conveying system,visual system and sorting system. The size of navel orange,the number of surface defects and the defect area were detected by the proposed system. The fruit was automatically graded according to the predetermined comprehensive assessment standard. The experimental results show that the average detection time of single picture is less than 30 ms,and the detection error of size is less than 3% with defect detection rate of 99% and defect area detection error less than 7%. Compared with the traditional detection system,the proposed system has the advantages of high detection speed and compact design,which is suitable for in-filed grading of navel orange.
To meet the demand of fruit farmers' timely grading of navel oranges,a set of navel orange postharvest field grading system was designed based on machine vision,which was composed of conveying system,visual system and sorting system. The size of navel orange,the number of surface defects and the defect area were detected by the proposed system. The fruit was automatically graded according to the predetermined comprehensive assessment standard. The experimental results show that the average detection time of single picture is less than 30 ms,and the detection error of size is less than 3% with defect detection rate of 99% and defect area detection error less than 7%. Compared with the traditional detection system,the proposed system has the advantages of high detection speed and compact design,which is suitable for in-filed grading of navel orange.
引文
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[13]林明盛,吴磊,柯科勇.视觉测量中摄像机标定精度评估方法[J].机床与液压,2016,44(23):20-22.LIN M S,WU L,KE K Y.Camera calibration precision evaluation method based on vision measurement[J].Machine Tool&Hydraulics,2016,44(23):20-22.(in Chinese)
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[15]SUMAN S,HUSSIN F A,MALIK A S,et al.Image enhancement using geometric mean filter and gamma correction for WCE images[J].International Conference on Neural Information Processing,2014,8836:276-283.
[2]李江波,饶秀勤,应义斌.基于照度-反射模型的脐橙表面缺陷检测[J].农业工程学报,2011,27(7):338-342.LI J B,RAO X Q,YING Y B.Detection of navel surface defects based on illumination-reflectance model[J].Transactions of the Chinese Society of Agricultural Engineering,2011,27(7):338-342.(in Chinese)
[3]ALEIXOS N,BLASCO J,NAVARRON F,et al.Multispectral inspection of citrus in real-time using machine vision and digital signal processors[J].Computers&Electronics in Agriculture,2002,33(2):121-137.
[4]葛纪帅,赵春江,黄文倩,等.基于智能称重的水果分级生产线设计[J].农机化研究,2012,34(1):132-136.GE J S,ZHAO C J,HUANG W Q,et al.Study on the production line for the fruit grading based on intelligent weighting[J].Journal of Agricultural Mechanization Research,2012,34(1):132-136.(in Chinese)
[5]程德明.GFX-8水果重量分选机设计[J].福建农机,2010(3):34-36.CHENG D M.Design of GFX-8 fruit weight sorter[J].Fujian Agricultural Machinery,2010(3):34-36.(in Chinese)
[6]胡发焕,董增文.基于机器视觉的脐橙品质在线分级检测[J].湖北农业科学,2014(9):112-118.HU F H,DONG Z W.Online grade detection of navel orange quality based on machine vision[J].Hubei Agricultural Sciences,2014(9):112-118.(in Chinese)
[7]AVHAD M B,TURKANE S M.ARM based fruit grading and management system using image processing[J].International Journal of Advanced Research in Computer Engineering&Technology,2013,2(1):243-248.
[8]MER BARI爦,GZEL E.Inspection and grading of agricultural products under different lighting conditions by computer vision technology[J].Journal of Agricultural Machinery Science,2013,9(2):117-125.
[9]JIANG J,GU J W.Recovering spectral reflectance under commonly available lighting conditions[C]∥2012IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.Piscataway:IEEE Computer Society,2012:1-8.
[10]ZOU X B,ZHAO J W,LI Y,et al.In-line detection of apple defects using three color cameras system[J].Computers&Electronics in Agriculture,2010,70(1):129-134.
[11]CHACON-MURGUIA M I,RAMIREZ-QUINTANA J.Segmentation of video background regions based on a DTCNN-clustering approach[J].Signal,Image and Video Processing,2015,9(1):135-144.
[12]赵杰文,刘文彬,邹小波.基于三摄像系统的苹果缺陷快速判别[J].江苏大学学报(自然科学版),2006,27(4):287-290.ZHAO J W,LIU W B,ZOU X B.Fast identification of apple defect based on three imaging system[J].Journal of Jiangsu University(Natural Science Edition),2006,27(4):287-290.(in Chinese)
[13]林明盛,吴磊,柯科勇.视觉测量中摄像机标定精度评估方法[J].机床与液压,2016,44(23):20-22.LIN M S,WU L,KE K Y.Camera calibration precision evaluation method based on vision measurement[J].Machine Tool&Hydraulics,2016,44(23):20-22.(in Chinese)
[14]李江波.脐橙表面缺陷的快速检测方法研究[D].杭州:浙江大学,2012.
[15]SUMAN S,HUSSIN F A,MALIK A S,et al.Image enhancement using geometric mean filter and gamma correction for WCE images[J].International Conference on Neural Information Processing,2014,8836:276-283.