基于颜色与面积特征的方格蔟蚕茧分割定位算法与试验
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摘要
蚕虫上蔟多采用纸板方格蔟,但纸板方格蔟在使用过程中会因扭曲变形导致方格分布不规则,而采茧机械对变形的方格蔟进行蚕茧采摘时,会对方格蔟造成损伤。为了提高方格蔟机械采茧的智能化水平,减少采茧设备对方格蔟的损伤,提出一种基于颜色与面积特征的方格蔟蚕茧分割定位算法,实现对方格蔟中蚕茧的分割、中心点定位和位置坐标的视觉测量。首先采用图像空间的Brown畸变模型对方格蔟图像进行畸变矫正,减小径向畸变对视觉测量的影响;对矫正后的图像采用Mean Shift聚类算法进行预分割,消除光照及图像背景对蚕茧分割的影响;然后对阈值分割和形态学处理后的二值化蚕茧图像进行基于面积特征的连通域标定,得到每个蚕茧中心点位置;将连通域标定得到的蚕茧中心点坐标代入图像坐标系与世界坐标系转换方程,得到每个蚕茧在笛卡尔空间的三维坐标,经过视觉测量确定蚕茧在方格蔟中的具体位置,控制蚕茧采摘装置采摘方格蔟中的蚕茧。经过试验,该算法对方格蔟中的蚕茧检测正确率为96.88%,蚕茧坐标最大定位偏差小于6.0 mm,满足采茧装置对蚕茧采摘的定位精度要求。
To improve the lower efficiency of silkworm cocoon harvesting,an algorithm of cocoon image segmentation and coordinate location was proposed based on color and area characteristics,and a cocoon harvestor was designed based on machine vision. The monocular CMOS camera was firstly used in the algorithm to take image of checker cocooning frame. And the non-measurement distortion correction method was used to correct the image. Secondly,the camera model was calibrated with the internal parameters for the monocular two-dimensional visual measurement system. The image was smoothed via gray and mean shift filter method because the outer floss of the cocoon can cause wrong segmentation of the image in checker cocooning frame image. Then the binary image was obtained by threshold segmentation. Next,the binary image was processed by open operation and area feature extraction method to remove noise region. A part of the smaller noise connected components can be removed by the open operation. The cocoon region can be extracted by the area characteristic when the large area of the connected components can be removed. The center point coordinates of the cocoon region were got by the connected components calibration,and were mapped into the world coordinates through the equation that transformed image coordinates to world coordinates to get the cocoons' positions in the Cartesian space.Finally,the cocoons were harvested by the cocoon harvestor. According to the experiment,the algorithm had the accuracy rate of 96. 88% for the cocoon detection in the checker cocooning frame and less than 6. 0 mm for the cocoon coordinate,which satisfied the requirement of the location of cocoon harvesting.
To improve the lower efficiency of silkworm cocoon harvesting,an algorithm of cocoon image segmentation and coordinate location was proposed based on color and area characteristics,and a cocoon harvestor was designed based on machine vision. The monocular CMOS camera was firstly used in the algorithm to take image of checker cocooning frame. And the non-measurement distortion correction method was used to correct the image. Secondly,the camera model was calibrated with the internal parameters for the monocular two-dimensional visual measurement system. The image was smoothed via gray and mean shift filter method because the outer floss of the cocoon can cause wrong segmentation of the image in checker cocooning frame image. Then the binary image was obtained by threshold segmentation. Next,the binary image was processed by open operation and area feature extraction method to remove noise region. A part of the smaller noise connected components can be removed by the open operation. The cocoon region can be extracted by the area characteristic when the large area of the connected components can be removed. The center point coordinates of the cocoon region were got by the connected components calibration,and were mapped into the world coordinates through the equation that transformed image coordinates to world coordinates to get the cocoons' positions in the Cartesian space.Finally,the cocoons were harvested by the cocoon harvestor. According to the experiment,the algorithm had the accuracy rate of 96. 88% for the cocoon detection in the checker cocooning frame and less than 6. 0 mm for the cocoon coordinate,which satisfied the requirement of the location of cocoon harvesting.
引文
1陈超科.方格蔟自动采茧机的研制[D].泰安:山东农业大学,2017.
2 川名茂,沈云岳.全自动选茧机的开发[J].丝绸,1988(11):44-45.
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7 甘勇.基于图像处理技术的干茧茧壳厚度计算方法[J].丝绸,2006(11):46-48.GAN Yong.Calculation method for shell thickness of dried cocoon based on image processing[J].Journal of Silk,2006(11):46-48 .(in Chinese)
8 乔乐领.基于图像处理的蚕茧自动计数系统的研究[D].杭州:浙江理工大学,2013.
9 陈浩,杨峥,刘霞,等.基于MATLAB的桑蚕茧选茧辅助检验方法的研究[J].丝绸,2016(3):32-36.CHEN Hao,YANG Zheng,LIU Xia,et al.Study on auxiliary testing method for mulberry silkworm cocoon sorting based on MATLAB[J].Journal of Silk,2016(3):32-36.(in Chinese)
10 马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京:科学出版社,1998:52.
11 CHATTERJEE C,ROYCHOWDHURY V P.Algorithms for coplanar camera calibration[J].Machine Vision and Applications,2000,12(2):84-97.
12 高瞻宇,顾营迎,刘宇航,等.采用简化Brown模型及改进BFGS法的相机自标定[J].光学精密工程,2017,25(9):2532-2540.GAO Zhanyu,GU Yingying,LIU Yuhang,et al.Self-calibration based on simplified Brown non-linear camera model and modified BFGS algorithm[J].Optics and Precision Engineering,2017,25(9):2532-2540.(in Chinese)
13 XU D,LI Y F,TAN M.A method for calibration cameras with large distortion in lens[J].Optical Engineering,2006,45(4):0436021-0436028.
14 吕朝辉,陈晓光,吴文福,等.基于双目立体视觉的秧苗直立度自动测定系统[J].农业机械学报,2002,33(1):60-62.LChaohui,CHEN Xiaoguang,WU Wenfu,et al.Automatic measuring system of seeding perpendicularity based on bonocular stereo vision[J].Transactions of the Chinese Society for Agricultural Machinery,2002,33(1):60-62.(in Chinese)
15 CONMANICIU D,MEER P.Mean shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
16 胡炼,罗锡文,曾山,等.基于机器视觉的株间机械除草装置的作物是别与定位方法[J].农业工程学报,2013,29(10):12-18.HU Lian,LUO Xiwen,ZENG Shan,et al.Plant recongnition and localization for intra-row mechanical weeding device based on machine vision[J].Transactions of the CSAE,2013,29(10):12-18.(in Chinese)
17 丁为民,赵思琪,赵三秦,等.基于机器视觉的果树树冠体积测量方法研究[J/OL].农业机械学报,2016,47(6):1-10.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20160601&journal_id=jcsam.DIO:10.6041/j.issn.1000-1298.2016.06.001.DING Weimin,ZHAO Siqi,ZHAO Sanqin,et all.Measurement methods of fruit tree canopy volume based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):1-10.(in Chinese)
18 MARAGOS P.Differential morphology and image processing[J].IEEE Transactions on Image Processing,1996,5(6):922-937.
19 李冠林,马占鸿,王海光.基于支持向量机的小麦条锈病和叶锈病图像识别[J].中国农业大学学报,2012,17(2):72-79.LI Guanlin,MA Zhanhong,WANG Haiguang.Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine[J].Journal of China Agricultural University,2012,17(2):72-79.(in Chinese)
20 宋宇,刘永博,刘路,等.基于机器视觉的玉米根茎导航基准线提取方法[J/OL].农业机械学报,2017,48(2):38-44.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170205&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.02.005.SONG Yu,LIU Yongbo,LIU Lu,et al.Extraction method of vavigation baseline of corn roots based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(2):38-44.(in Chinese)
21 GONZALEZ R C,WOODS R E.数字图像处理[M].阮秋琦,阮宇智,译.2版.北京:电子工业出版社,2003:423-424.
22 蒋焕煜,彭永石,申川,等.基于双目立体视觉技术的成熟番茄识别与定位[J].农业工程学报,2008,24(8):279-283.JIANG Huanyu,PENG Yongshi,SHEN Chuan,et al.Recognizing and locating ripe tomatoes based on binocular stereo vision technology[J].Transactions of the CSAE,2008,24(8):279-283.(in Chinese)
23 沈宝国,陈树人,尹建军,等.基于颜色特征的棉田绿色杂草图像识别方法[J].农业工程学报,2009,25(6):163-167.SHEN Baoguo,CHEN Shuren,YIN Jianjun,et al.Image recognition of green weeds in cotton fields based on color feature[J].Transactions of the CSAE,2009,25(6):163-167.(in Chinese)
24 SUN Qiucheng,HOU Yueqian,TAN Qingchang.A new method of camera calibration based on segmentation model[J].Optik,2013,124(24):6991-6995.
25 汤一平,王伟羊,朱威,等.基于机器视觉的茶陇识别与采茶机导航方法[J/OL].农业机械学报,2016,47(1):45-50.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20160107&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2016.01.007.TANG Yiping,WANG Weiyang,ZHU Wei,et al.Tea ridge identification and navigation method for tea-plucking machine based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):45-50.(in Chinese)
26 王跃勇,于海业,刘媛媛.基于双目立体视觉的机械手移栽穴盘定位方法[J].农业工程学报,2016,32(5):43-49.WANG Yueyong,YU Haiye,LIU Yuanyuan.Mechanical transplanting plug tray localization method based on binocular stereo vision[J].Transactions of the CSAE,2016,32(5):43-49.(in Chinese)
27 尹建军,沈宝国,陈树人,等.基于机器视觉的田间杂草定位技术[J].农业机械学报,2010,41(6):163-166.YIN Jianjun,SHEN Baoguo,CHEN Shuren,et al.Position technology of filed weed based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(6):163-166.(in Chinese)
28 黄福珍,苏剑波.人脸检测[M].上海:上海交通大学出版社,2006:12.
2 川名茂,沈云岳.全自动选茧机的开发[J].丝绸,1988(11):44-45.
3 蔡健荣,周小军,李玉良,等.基于机器视觉自然场景下成熟柑橘识别[J].农业工程学报,2008,24(1):175-178.CAI Jianrong,ZHOU Xiaojun,LI Yuliang,et al.Recognition of mature orange in natural scene based on machine vision[J].Transactions of the CSAE,2008,24(1):175-178.(in Chinese)
4 金航峰.基于光谱和高光谱图像技术的蚕茧品质无损检测研究[D].杭州:浙江大学,2013.
5 宋亚杰,谢守勇,冉瑞龙.机器视觉技术在蚕茧无损检测中的应用研究[J].现代农业装备,2006(9):48-51.SONG Yajie,XIE Shouyong,RAN Ruilong.Applied research on machine vision technology in non-destructive test of cocoon[J].Modern Agricultural Equipments,2006(9):48-51.(in Chinese)
6 周志宇,刘喜昂,杨东鹤.机器视觉在蚕茧表面积测量中的应用[J].纺织学报,2006,27(12):29-31.ZHOU Zhiyu,LIU Xi'ang,YANG Donghe.Application of machine vision in measurement of cocoon superficial area[J].Journal of Textile Research,2006,27(12):29-31.(in Chinese)
7 甘勇.基于图像处理技术的干茧茧壳厚度计算方法[J].丝绸,2006(11):46-48.GAN Yong.Calculation method for shell thickness of dried cocoon based on image processing[J].Journal of Silk,2006(11):46-48 .(in Chinese)
8 乔乐领.基于图像处理的蚕茧自动计数系统的研究[D].杭州:浙江理工大学,2013.
9 陈浩,杨峥,刘霞,等.基于MATLAB的桑蚕茧选茧辅助检验方法的研究[J].丝绸,2016(3):32-36.CHEN Hao,YANG Zheng,LIU Xia,et al.Study on auxiliary testing method for mulberry silkworm cocoon sorting based on MATLAB[J].Journal of Silk,2016(3):32-36.(in Chinese)
10 马颂德,张正友.计算机视觉——计算理论与算法基础[M].北京:科学出版社,1998:52.
11 CHATTERJEE C,ROYCHOWDHURY V P.Algorithms for coplanar camera calibration[J].Machine Vision and Applications,2000,12(2):84-97.
12 高瞻宇,顾营迎,刘宇航,等.采用简化Brown模型及改进BFGS法的相机自标定[J].光学精密工程,2017,25(9):2532-2540.GAO Zhanyu,GU Yingying,LIU Yuhang,et al.Self-calibration based on simplified Brown non-linear camera model and modified BFGS algorithm[J].Optics and Precision Engineering,2017,25(9):2532-2540.(in Chinese)
13 XU D,LI Y F,TAN M.A method for calibration cameras with large distortion in lens[J].Optical Engineering,2006,45(4):0436021-0436028.
14 吕朝辉,陈晓光,吴文福,等.基于双目立体视觉的秧苗直立度自动测定系统[J].农业机械学报,2002,33(1):60-62.LChaohui,CHEN Xiaoguang,WU Wenfu,et al.Automatic measuring system of seeding perpendicularity based on bonocular stereo vision[J].Transactions of the Chinese Society for Agricultural Machinery,2002,33(1):60-62.(in Chinese)
15 CONMANICIU D,MEER P.Mean shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
16 胡炼,罗锡文,曾山,等.基于机器视觉的株间机械除草装置的作物是别与定位方法[J].农业工程学报,2013,29(10):12-18.HU Lian,LUO Xiwen,ZENG Shan,et al.Plant recongnition and localization for intra-row mechanical weeding device based on machine vision[J].Transactions of the CSAE,2013,29(10):12-18.(in Chinese)
17 丁为民,赵思琪,赵三秦,等.基于机器视觉的果树树冠体积测量方法研究[J/OL].农业机械学报,2016,47(6):1-10.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20160601&journal_id=jcsam.DIO:10.6041/j.issn.1000-1298.2016.06.001.DING Weimin,ZHAO Siqi,ZHAO Sanqin,et all.Measurement methods of fruit tree canopy volume based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):1-10.(in Chinese)
18 MARAGOS P.Differential morphology and image processing[J].IEEE Transactions on Image Processing,1996,5(6):922-937.
19 李冠林,马占鸿,王海光.基于支持向量机的小麦条锈病和叶锈病图像识别[J].中国农业大学学报,2012,17(2):72-79.LI Guanlin,MA Zhanhong,WANG Haiguang.Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine[J].Journal of China Agricultural University,2012,17(2):72-79.(in Chinese)
20 宋宇,刘永博,刘路,等.基于机器视觉的玉米根茎导航基准线提取方法[J/OL].农业机械学报,2017,48(2):38-44.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170205&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.02.005.SONG Yu,LIU Yongbo,LIU Lu,et al.Extraction method of vavigation baseline of corn roots based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(2):38-44.(in Chinese)
21 GONZALEZ R C,WOODS R E.数字图像处理[M].阮秋琦,阮宇智,译.2版.北京:电子工业出版社,2003:423-424.
22 蒋焕煜,彭永石,申川,等.基于双目立体视觉技术的成熟番茄识别与定位[J].农业工程学报,2008,24(8):279-283.JIANG Huanyu,PENG Yongshi,SHEN Chuan,et al.Recognizing and locating ripe tomatoes based on binocular stereo vision technology[J].Transactions of the CSAE,2008,24(8):279-283.(in Chinese)
23 沈宝国,陈树人,尹建军,等.基于颜色特征的棉田绿色杂草图像识别方法[J].农业工程学报,2009,25(6):163-167.SHEN Baoguo,CHEN Shuren,YIN Jianjun,et al.Image recognition of green weeds in cotton fields based on color feature[J].Transactions of the CSAE,2009,25(6):163-167.(in Chinese)
24 SUN Qiucheng,HOU Yueqian,TAN Qingchang.A new method of camera calibration based on segmentation model[J].Optik,2013,124(24):6991-6995.
25 汤一平,王伟羊,朱威,等.基于机器视觉的茶陇识别与采茶机导航方法[J/OL].农业机械学报,2016,47(1):45-50.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20160107&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2016.01.007.TANG Yiping,WANG Weiyang,ZHU Wei,et al.Tea ridge identification and navigation method for tea-plucking machine based on machine vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):45-50.(in Chinese)
26 王跃勇,于海业,刘媛媛.基于双目立体视觉的机械手移栽穴盘定位方法[J].农业工程学报,2016,32(5):43-49.WANG Yueyong,YU Haiye,LIU Yuanyuan.Mechanical transplanting plug tray localization method based on binocular stereo vision[J].Transactions of the CSAE,2016,32(5):43-49.(in Chinese)
27 尹建军,沈宝国,陈树人,等.基于机器视觉的田间杂草定位技术[J].农业机械学报,2010,41(6):163-166.YIN Jianjun,SHEN Baoguo,CHEN Shuren,et al.Position technology of filed weed based on machine vision[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(6):163-166.(in Chinese)
28 黄福珍,苏剑波.人脸检测[M].上海:上海交通大学出版社,2006:12.