机器视觉油炸鱼豆腐外观缺陷识别与剔除系统
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摘要
设计了外观缺陷识别与剔除试验系统,利用产品图像的颜色特征和形状特征,可实现油炸鱼豆腐外观质量的在线检测、识别与自动剔除.在产品外观颜色检测中利用RGB颜色空间中的B分量阈值作为特征;在产品外形缺陷检测中采用目标面积和边缘形状矩形度实现特征识别.经排序机构将产品有序排列后,根据各列缺陷识别结果,由单片机控制剔除装置完成相应动作,剔除不合格品.实验表明:该试验系统可有效完成油炸鱼豆腐产品的在线外观检测及残次品的剔除,提升生产线成套装备的技术水平.
A detecting and eliminating system to deal with appearance defects of fired-fish-bean-curd has been developed,which makes use of color and shape features to detect,recognize and eliminate products of bad appearance on line.It takes the B component threshold in RGB color space as the criterion for the color appearance of the product;the area and degree of similarity of the image with rectangle are used to distinguish defects in shape appearance.The defect recognition results of each line can be translated into special information code automatically after the products are sorted into order by the sorting mechanism,and then the elimination device will use SCM to reject unqualified products automatically.Experimental results show that this system can be applied to fried-fish-bean-curd appearance detection and elimination,and thus improve the technology and quality of the production line.
A detecting and eliminating system to deal with appearance defects of fired-fish-bean-curd has been developed,which makes use of color and shape features to detect,recognize and eliminate products of bad appearance on line.It takes the B component threshold in RGB color space as the criterion for the color appearance of the product;the area and degree of similarity of the image with rectangle are used to distinguish defects in shape appearance.The defect recognition results of each line can be translated into special information code automatically after the products are sorted into order by the sorting mechanism,and then the elimination device will use SCM to reject unqualified products automatically.Experimental results show that this system can be applied to fried-fish-bean-curd appearance detection and elimination,and thus improve the technology and quality of the production line.
引文
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[2]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识别与定位方法[J].农业工程学报,2013,29(10):2–18.
[3]Heiskanen V,Marjanen K,Kallio P.Machine vision based measurement of dynamic contact angles in microchannel flows[J].Journal of Bionic Engineering,2008,5(4):282–290.
[4]周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7):178–183.
[5]赵茂程,候文军.我国基于机器视觉的水果自动分级技术及研究进展[J].包装与食品机械,2007,25(5):5–8.
[6]周聪玲,宋振国,王永强,等.油炸鱼豆腐外观质量检测方法研究[J].粮油加工,2015(3):46–50.
[7]张兰凤,郭丹.利用Matlab软件对医学彩色图像的空间转换[J].科技创新与应用,2015(29):43.
[8]陈蕴,陈松.基于动态范围调整的指纹图像增强方法[J].计算机工程与应用,2015,51(8):183–188.
[9]朱齐丹,荆丽秋,毕荣生,等.最小误差阈值分割法的改进算法[J].光电工程,2015,37(7):107–113.
[10]杨力,李东新.基于遗传算法的图像阈值分割的研究[J].信息技术,2015(11):116–120.
[11]孙丽媛,杨鸿玉,王汝梅.图像形状匹配及特征识别在铁矿石熔点检测中的应用研究[J].工业控制计算机,2010,23(6):31–32.
[12]卢蓉,范勇,陈念年,等.一种提取目标图像最小外接矩形的快速算法[J].计算机工程,2010,36(21):178–180.
[13]李洋,李岳阳.一种快速提取植物叶片最小外接矩形的算法[J].江南大学学报:自然科学版,2015,14(3):273–277.