CN105699397B - 一种苹果表面光泽度检测方法 - Google Patents
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Abstract
本发明公开一种苹果表面光泽度检测方法,属于计算机视觉无损检测领域。该方法先使用计算机视觉系统拍摄苹果图像,然后用自适应双峰法阈值分割得到苹果二值图像,再用定面积阈值分割得到苹果高亮区域二值图像,然后提取苹果高亮区域的平均R、G、B和灰度值以及灰度值的标准差,最后将这5个参数输入SVM模型对其光泽度进行分级。该方法可以快速准确地对苹果光泽度进行三等级的分级,为苹果生产商品化应用提供支持。
Description
技术领域:
本发明属于计算机视觉无损检测领域,特别涉及一种基于计算机视觉的苹果表面光泽度检测方法。
背景技术:
苹果是世界上栽培面积最大,产量最多的水果之一,也是日常生活中最常见的水果。光泽度是苹果重要的感官指标,可以间接反映出果蔬的成熟度、新鲜度以及内在品质,也是消费者评判苹果价值的重要参考。生产商使用可食性膜对苹果处理,不仅可以保持新鲜度,而且可以增加果实光泽度。光泽度高的苹果更能够激发消费者的购买欲望,拥有较高的附加值。随着果蔬商品化发展的进程,苹果的光泽度成为越来越受人关注的指标。
光泽度是物体表面接近镜面的程度,光泽度高的苹果在日常光源照射下,表面会出现较强的镜面反射,人眼可观察到果蔬表面有较大的反光区域。苹果光泽度的检测一般是通过人工分拣完成,主观性较强,容易受到不均匀的照明条件和苹果本身颜色的干扰。目前,非平面物体的光泽度一般用小孔光泽度仪进行测量。为了将曲面近似为平面,小孔光泽度仪每次测量只能涵盖极小的区域,检测苹果时,需进行多次测量才能评判其光泽度,无法适应生产要求。因此,高效准确的苹果表面光泽度检测方法是目前该领域需要解决的技术问题。
发明内容:
本发明的目的是提供一种苹果表面光泽度检测方法,该方法可以快速准确地对苹果按照光泽度进行三等级的分级。
为了解决上述问题,本发明提供了一种1.一种苹果表面光泽度检测方法,其步骤为:先使用计算机视觉系统拍摄苹果图像,然后提取苹果图像参数,最后用支持向量机模型对其光泽度进行分级,其特征在于:
1)苹果图像拍摄系统包含:工业相机(1)、2根33cm 12W条状光源(2)、底座(3)、暗箱(4)和苹果托盘(5),其中苹果托盘(5)固定在底座(3)中央,底座(3)和托盘(5)均为黑色,工业相机(1)固定在底座(3)上方,使底座到镜头距离为30cm,2根条状光源(2)固定在苹果托盘两侧,距底座15cm,2根条状光源(2)间距为20cm,图像采集时将待测苹果放在托盘(5)上,打开条状光源(2),然后用工业相机(1)进行拍摄,得到苹果的彩色图像;
2)提取苹果图像参数方法为:先用自适应的双峰阈值分割方法分割对苹果的彩色图像进行分割,得到苹果区域的二值图像,并计算二值图像中苹果区域的面积,然后以该面积的1/20为目标面积,对灰度化的苹果图像进行定面积的阈值分割,获得苹果区域中面积为目标面积的高亮区域二值图像,再以高亮区域二值图像为模板,计算原图像的中高亮区域的平均R、G、B值以及苹果灰度图像中的平均灰度值和标准差;
3)支持向量机模型分级方法为:将原图高亮区域的平均R、G、B值以及灰度图像高亮区域的平均灰度值和标准差作为5个特征参数,输入1v1策略的三分类支持向量机模型中,得出分类结果;
其中,所使用的1v1策略的三分类支持向量机模型包含了3个支持向量机分类模型,采用径向基函数为核函数,gamma值为0.00005,区分一级和二级的SVM分类模型中包含4个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)208.5592,239.3563,201.4312,164.4992,0.0143
(4)228.9160,249.5293,231.5520,161.1120,0.0074
拉格朗日系数分别为:85.5531,8.7885,-59.9985,-34.3431,判别函数的常数项为-0.3211;
区分一级和三级的SVM分类模型中包含3个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)224.8338,238.0791,221.4244,207.6381,0.0360
拉格朗日系数分别为:6.0352,11.7521,-17.7873,判别函数的常数项为-0.9182;
区分二级和三级的SVM分类模型中包含17个支持向量,分别为:
(1)233.3792221,246.107545,233.5508846,199.069089,0.01429046
(2)236.294476,248.7345096,236.1741231,204.1484982,0.013425064
(3)231.7120675,244.7128706,230.0568235,206.2040024,0.024079656
(4)224.0930734,243.9697221,219.9167151,193.5313978,0.033659825
(5)241.8603836,251.4054233,243.3650132,209.5105159,0.00965339
(6)238.2642872,247.3274435,241.0505856,200.5860763,0.0076752
(7)241.7954878,250.6154433,244.5164919,205.1496028,0.008257869
(8)238.9173945,249.7086608,239.9163755,205.6566958,0.01004152
(9)229.4414129,242.964749,227.7038917,202.838621,0.017339417
(10)227.884238,245.0573444,225.5924563,194.7347439,0.041349383
(11)234.2328776,246.6701208,235.2076767,196.6584095,0.01411076
(12)243.6197448,251.0233565,245.7454142,213.7620485,0.012430966
(13)244.1270541,250.9962926,245.863978,217.4620741,0.019465918
(14)234.4471492,245.3351713,234.6794345,204.7094087,0.024458587
(15)232.0185506,242.644714,231.3059246,207.7550549,0.018015919
(16)238.5212553,247.1928826,239.5274021,210.6466516,0.031216588
(17)237.5333816,246.0849156,238.1493402,211.943371,0.030936672
拉格朗日系数分别为:100,100,100,100,100,31.6,100,100,-100,-100,-100,-100,-50.0421,-100,-56.1938,-100,-25.3641,判别函数的常数项为0.2178。
附图说明:
图1:计算机视觉系统
图2:苹果彩色图像
图3:苹果区域二值图像
图4:高亮区域二值图像
具体实施方式:
本发明的目的是提供一种苹果表面光泽度的检测方法,该方法可以快速准确地对苹果表面光泽度进行三等级的分级。
为了使本技术领域人员更好地理解本发明方案,下面结合具体实施方式对本发明作进一步的详细说明。
在一种具体实施例中,测量苹果的光泽度。先将待测苹果放在托盘(5)上,打开条状光源(2),然后用工业相机(1)进行拍摄,得到苹果的彩色图像,然后用自适应的双峰阈值分割方法分割对苹果的彩色图像进行分割,得到苹果区域的二值图像,并计算二值图像中苹果区域的面积,然后以该面积的1/20为目标面积,对灰度化的苹果图像进行定面积的阈值分割,获得苹果区域中面积为目标面积的高亮区域二值图像,再以高亮区域二值图像为模板,计算原图像的中高亮区域的平均R、G、B值以及苹果灰度图像中的平均灰度值和标准差。然后,将原图高亮区域的平均R、G、B值以及灰度图像高亮区域的平均灰度值和标准差作为5个特征参数,输入1v1策略的三分类支持向量机模型中,得出分类结果;其中,所使用的1v1策略的三分类支持向量机模型包含了3个支持向量机分类模型,采用径向基函数为核函数,gamma值为0.00005,区分一级和二级的SVM分类模型中包含4个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)208.5592,239.3563,201.4312,164.4992,0.0143
(4)228.9160,249.5293,231.5520,161.1120,0.0074
拉格朗日系数分别为:85.5531,8.7885,-59.9985,-34.3431,判别函数的常数项为-0.3211;
区分一级和三级的SVM分类模型中包含3个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)224.8338,238.0791,221.4244,207.6381,0.0360
拉格朗日系数分别为:6.0352,11.7521,-17.7873,判别函数的常数项为-0.9182;
区分二级和三级的SVM分类模型中包含17个支持向量,分别为:
(1)233.3792221,246.107545,233.5508846,199.069089,0.01429046
(2)236.294476,248.7345096,236.1741231,204.1484982,0.013425064
(3)231.7120675,244.7128706,230.0568235,206.2040024,0.024079656
(4)224.0930734,243.9697221,219.9167151,193.5313978,0.033659825
(5)241.8603836,251.4054233,243.3650132,209.5105159,0.00965339
(6)238.2642872,247.3274435,241.0505856,200.5860763,0.0076752
(7)241.7954878,250.6154433,244.5164919,205.1496028,0.008257869
(8)238.9173945,249.7086608,239.9163755,205.6566958,0.01004152
(9)229.4414129,242.964749,227.7038917,202.838621,0.017339417
(10)227.884238,245.0573444,225.5924563,194.7347439,0.041349383
(11)234.2328776,246.6701208,235.2076767,196.6584095,0.01411076
(12)243.6197448,251.0233565,245.7454142,213.7620485,0.012430966
(13)244.1270541,250.9962926,245.863978,217.4620741,0.019465918
(14)234.4471492,245.3351713,234.6794345,204.7094087,0.024458587
(15)232.0185506,242.644714,231.3059246,207.7550549,0.018015919
(16)238.5212553,247.1928826,239.5274021,210.6466516,0.031216588
(17)237.5333816,246.0849156,238.1493402,211.943371,0.030936672
拉格朗日系数分别为:100,100,100,100,100,31.6,100,100,-100,-100,-100,-100,-50.0421,-100,-56.1938,-100,-25.3641,判别函数的常数项为0.2178。
具体实施例
在一种具体实施例中,测量苹果的光泽度。先将待测苹果放在托盘(5)上,打开条状光源(2),然后用工业相机(1)进行拍摄,得到苹果的彩色图像,如图2所示,再用自适应的双峰阈值分割方法分割对苹果的彩色图像进行分割,得到苹果区域的二值图像,如图3所示,并计算出为二值图像中苹果区域的面积为284506像素,然后用苹果彩色图像的灰度直方图计算出分割得到1/20的原图面积的二值图像所需的灰度值为221,再以221灰度值为阈值分割苹果彩色图像,获得苹果区域中面积为14225像素的高亮区域二值图像,如图4所示,再以高亮区域二值图像为模板,计算原图像的中高亮区域的平均R、G、B值以及苹果灰度图像中的平均灰度值和标准差,然后将原图高亮区域的平均R、G、B值以及灰度图像高亮区域的平均灰度值和标准差作为5个特征参数,分别为232.53、241.61、231.22、215.47、0.032。将这5个参数输入区分一级和二级光泽度的模型中,该模型采用径向基函数为核函数,gamma值为0.00005,包含4个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)208.5592,239.3563,201.4312,164.4992,0.0143
(4)228.9160,249.5293,231.5520,161.1120,0.0074
拉格朗日系数分别为:85.5531,8.7885,-59.9985,-34.3431,判别函数的常数项为-0.3211,参数带入后得到结果为该苹果光泽度为一级,再将这5个参数输入区分一级和三级光泽度的模型中,该模型采用径向基函数为核函数,gamma值为0.00005,包含3个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)224.8338,238.0791,221.4244,207.6381,0.0360
拉格朗日系数分别为:6.0352,11.7521,-17.7873,判别函数的常数项为-0.9182,参数带入后得到结果为该苹果光泽度为一级,然后将这5个参数输入区分二级和三级光泽度的模型中,该模型采用径向基函数为核函数,gamma值为0.00005,包含17个支持向量,分别为:
(1)233.3792221,246.107545,233.5508846,199.069089,0.01429046
(2)236.294476,248.7345096,236.1741231,204.1484982,0.013425064
(3)231.7120675,244.7128706,230.0568235,206.2040024,0.024079656
(4)224.0930734,243.9697221,219.9167151,193.5313978,0.033659825
(5)241.8603836,251.4054233,243.3650132,209.5105159,0.00965339
(6)238.2642872,247.3274435,241.0505856,200.5860763,0.0076752
(7)241.7954878,250.6154433,244.5164919,205.1496028,0.008257869
(8)238.9173945,249.7086608,239.9163755,205.6566958,0.01004152
(9)229.4414129,242.964749,227.7038917,202.838621,0.017339417
(10)227.884238,245.0573444,225.5924563,194.7347439,0.041349383
(11)234.2328776,246.6701208,235.2076767,196.6584095,0.01411076
(12)243.6197448,251.0233565,245.7454142,213.7620485,0.012430966
(13)244.1270541,250.9962926,245.863978,217.4620741,0.019465918
(14)234.4471492,245.3351713,234.6794345,204.7094087,0.024458587
(15)232.0185506,242.644714,231.3059246,207.7550549,0.018015919
(16)238.5212553,247.1928826,239.5274021,210.6466516,0.031216588
(17)237.5333816,246.0849156,238.1493402,211.943371,0.030936672
拉格朗目系数分别为:100,100,100,100,100,31.6,100,100,-100,-100,-100,-100,-50.0421,-100,-56.1938,-100,-25.3641,判别函数的常数项为0.2178,参数带入后得到结果为该苹果光泽度为二级,最后通过1v1分类策略的SVM分类模型的评判规则判定该苹果光泽度等级为一级。用该方法对建模集200个不同光泽程度的苹果进行了光泽度分级,准确率为100%,对另外100个不同光泽程度的苹果进行验证,分级的准确率为97%,表明该方法可以满足工业生产的需要。
Claims (1)
1.一种苹果表面光泽度检测方法,其步骤为:先使用计算机视觉系统拍摄苹果图像,然后提取苹果图像参数,最后用三分类支持向量机模型对其光泽度进行判断,其特征在于:
1)苹果图像拍摄系统包含:工业相机(1)、2根33cm 12W条状光源(2)、底座(3)、暗箱(4)和苹果托盘(5),其中苹果托盘(5)固定在底座(3)中央,底座(3)和托盘(5)均为黑色,工业相机(1)固定在底座(3)上方,使底座到镜头距离为30cm,2根条状光源(2)固定在苹果托盘两侧,距底座15cm,2根条状光源(2)间距为20cm,图像采集时将待测苹果放在托盘(5)上,打开条状光源(2),然后用工业相机(1)进行拍摄,得到苹果的彩色图像;
2)提取苹果图像参数方法为:先用自适应的双峰阈值分割方法分割对苹果的彩色图像进行分割,得到苹果区域的二值图像,并计算二值图像中苹果区域的面积,然后以该面积的1/20为目标面积,对灰度化的苹果图像进行定面积的阈值分割,获得苹果区域中面积为目标面积的高亮区域二值图像,再以高亮区域二值图像为模板,计算原图像中的高亮区域的平均R、G、B值以及苹果灰度图像中的平均灰度值和标准差;
3)三分类支持向量机模型分级方法为:将原图高亮区域的平均R、G、B值以及灰度图像高亮区域的平均灰度值和标准差作为5个特征参数,输入1v1策略的三分类支持向量机模型中,得出分类结果;
其中,所使用的1v1策略的三分类支持向量机模型包含了3个支持向量机分类模型,采用径向基函数为核函数,gamma值为0.00005,区分一级和二级的SVM分类模型中包含4个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)208.5592,239.3563,201.4312,164.4992,0.0143
(4)228.9160,249.5293,231.5520,161.1120,0.0074
拉格朗日系数分别为:85.5531,8.7885,-59.9985,-34.3431,判别函数的常数项为-0.3211;
区分一级和三级的SVM分类模型中包含3个支持向量,分别为:
(1)214.1745,234.6124,216.1018,150.5813,0.0109
(2)213.2199,198.5613,220.7493,213.1268,0.0103
(3)224.8338,238.0791,221.4244,207.6381,0.0360
拉格朗日系数分别为:6.0352,11.7521,-17.7873,判别函数的常数项为-0.9182;
区分二级和三级的SVM分类模型中包含17个支持向量,分别为:
(1)233.3792221,246.107545,233.5508846,199.069089,0.01429046
(2)236.294476,248.7345096,236.1741231,204.1484982,0.013425064
(3)231.7120675,244.7128706,230.0568235,206.2040024,0.024079656
(4)224.0930734,243.9697221,219.9167151,193.5313978,0.033659825
(5)241.8603836,251.4054233,243.3650132,209.5105159,0.00965339
(6)238.2642872,247.3274435,241.0505856,200.5860763,0.0076752
(7)241.7954878,250.6154433,244.5164919,205.1496028,0.008257869
(8)238.9173945,249.7086608,239.9163755,205.6566958,0.01004152
(9)229.4414129,242.964749,227.7038917,202.838621,0.017339417
(10)227.884238,245.0573444,225.5924563,194.7347439,0.041349383
(11)234.2328776,246.6701208,235.2076767,196.6584095,0.01411076
(12)243.6197448,251.0233565,245.7454142,213.7620485,0.012430966
(13)244.1270541,250.9962926,245.863978,217.4620741,0.019465918
(14)234.4471492,245.3351713,234.6794345,204.7094087,0.024458587
(15)232.0185506,242.644714,231.3059246,207.7550549,0.018015919
(16)238.5212553,247.1928826,239.5274021,210.6466516,0.031216588
(17)237.5333816,246.0849156,238.1493402,211.943371,0.030936672
拉格朗日系数分别为:100,100,100,100,100,31.6,100,100,-100,-100,-100,-100,-50.0421,-100,-56.1938,-100,-25.3641,判别函数的常数项为0.2178。
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