黄瓜采摘机器人视觉关键技术及系统研究
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摘要
基于机器视觉的果蔬采摘机器人可充分利用其信息感知能力对果蔬进行识别,提高采摘效率,但是目前采摘机器人还未得到大量应用,其重要原因之一为果实识别率不高,因此研究采摘机器人的目标识别对加快农业采摘机器人的实用进程、改变目前主要依靠人工采摘的现状具有重要现实意义。
本文以温室黄瓜作为研究对象,在自然环境下采集的可见光图像中,由于黄瓜果实和叶茎颜色相近给识别带来了困难,采用基于形状特征的模板匹配法可实现黄瓜果实识别率为87%,采用双目立体视觉实现黄瓜采摘点的三维空间坐标定位,在物距500mm-750mm时,实现最大定位误差9.46mm。本文的主要研究内容和成果如下:
1、研究了黄瓜图像的颜色特征,分析了黄瓜果实和茎叶等在RGB模型和HSI模型中6个分量图像,通过对各分量图像的研究发现,黄瓜果实和茎叶等背景在G分量图像中差别较大,选择G分量图作为颜色特征作图像分割,通过图像增强、阈值分割、形态学操作等一系列处理过程,得到黄瓜图像分割结果,但分割结果并不是很理想。
2、研究了黄瓜果实形状的表示和描述方法。采集200幅简单背景下的黄瓜图像做处理并得到黄瓜边界,采用椭圆傅里叶描述边界并做归一化处理,通过平均各描述子得到平均椭圆傅里叶描述子,通过反变换描述子重建黄瓜边界,得到标准的黄瓜边界。
3、研究了模板变形和模板匹配算法。以得到的标准的黄瓜边界为基础,通过5次尺度变换和13次角度变换,得到65个不同尺度和不同角度的黄瓜形状,对其进行实体化后形成模板库。采用基于模板库的匹配算法对复杂背景下的100幅黄瓜图像进行灰度模板匹配,实现黄瓜识别,正确识别率达87%,对颜色相近以及部分遮挡的果实仍能实现识别,并提出匹配的模板起始点作为黄瓜果实顶点。
4、研究了双目立体视觉原理、数学模型以及摄像机标定方法。构建双目视觉系统并进行摄像机标定实验,得到双目立体视觉系统的内外参数,根据双目视觉原理,对黄瓜采摘点进行定位计算,得到其空间坐标,利用FARO ARM三坐标测量仪测量采摘点实际空间坐标,并与计算结果进行比较,相对于物距500mm~750mm,实现最大定位误差9.46mm,可满足满足采摘系统要求。
5、建立了一套黄瓜采摘机器人识别和定位系统。构建黄瓜采摘机器人的硬件系统和软件系统,实现了在自然环境下采集的可见光图像中黄瓜果实的识别和空间定位。
Fruit and vegetable harvesting robot based on machine vision could identify fruits and vegetables to improve picking efficiency by using the ability of information apperceiving, but harvesting robot has not been widely used. One of the most important reasons is that the fruits recognition rate is not high enough. Thus there would be important practical significance for accelerating the practical process on agricultural harvesting robot and changing the current status of the main rely on manual harvesting by studying the target recognition of harvesting robot.
In this thesis, cucumber in greenhouse was taken as the study object, in order to solve the problem on recognition between cucumber fruits and leaves or stem which have similar color, the visible light images of cucumber in natural environment was studied. The fruit could be identified correctly by using template matching which using shape features of cucumber, the recognition rate could achieve to 87%, the three-dimensional coordinate location of cucumber picking point could be calculated by using binocular stereo vision, the location error could achieved to 9.46mm. The main research contents and results in the thesis are as follows:
1. The color features of cucumber image were studied. The R, G, B and H, S, I component of cucumber fruit and leaf was analyzed, although there is some differences between fruit and leaf on G component but this difference is not enough identify the fruit from the background which could achieve ideal cucumber segmentation. An identification method based on cucumber fruit shape features was proposed. The cucumber fruit border was obtained by using basic image processing on a simple background cucumber image, including preprocessing, morphological operations, region labeling and edge detection.
2. Cucumber fruit shape representation and description methods were discussed. 200 pieces of different cucumbers were taken as the sample source, the cucumber images were taken and the fruit border was obtained by image processing. Then the boundary was described by using elliptic Fourier description and was normalized. The average elliptic Fourier descriptors waer obtained by averaging all the descriptors. After that the standard cucumber was obtained by inverse transform of boundary descriptors.
3. Template deformation and template matching method was proposed. After 5 times of scale transformation and 13 times of angle transformation on the standard boundary, 65 pieces of cucumber shapes with different size and different angle was achieved. The gray template matching was done on 100 pieces of cucumber image with complex background by using template matching algorithm based on template database, and the correct recognition rate is 87%. The fruit with partial occlusion and similar color can still be recognized correctly. The matching template starting point was taken as the fruit top point.
4. The principle, mathematical models and calibration methods of binocular stereo vision was discussed. A binocular vision system was established. The internal and external parameters of the binocular stereo vision system were obtained by camera calibration experiments. And the space coordinates of cucumber picking point was calculated using triangulation measurement method, and the actual position of picking point was measured by using FARO ARM, and the results were compared with the calculation results, the results showed that the max positioning error is 9.46mm relative to the object distance between 500mm and 750mm which could meet the requirement of the vision system..
5. The identification and positioning system for cucumber harvesting robot was established including the hardware and software systems which achieve the identification of cucumber fruits and space positioning.
本文以温室黄瓜作为研究对象,在自然环境下采集的可见光图像中,由于黄瓜果实和叶茎颜色相近给识别带来了困难,采用基于形状特征的模板匹配法可实现黄瓜果实识别率为87%,采用双目立体视觉实现黄瓜采摘点的三维空间坐标定位,在物距500mm-750mm时,实现最大定位误差9.46mm。本文的主要研究内容和成果如下:
1、研究了黄瓜图像的颜色特征,分析了黄瓜果实和茎叶等在RGB模型和HSI模型中6个分量图像,通过对各分量图像的研究发现,黄瓜果实和茎叶等背景在G分量图像中差别较大,选择G分量图作为颜色特征作图像分割,通过图像增强、阈值分割、形态学操作等一系列处理过程,得到黄瓜图像分割结果,但分割结果并不是很理想。
2、研究了黄瓜果实形状的表示和描述方法。采集200幅简单背景下的黄瓜图像做处理并得到黄瓜边界,采用椭圆傅里叶描述边界并做归一化处理,通过平均各描述子得到平均椭圆傅里叶描述子,通过反变换描述子重建黄瓜边界,得到标准的黄瓜边界。
3、研究了模板变形和模板匹配算法。以得到的标准的黄瓜边界为基础,通过5次尺度变换和13次角度变换,得到65个不同尺度和不同角度的黄瓜形状,对其进行实体化后形成模板库。采用基于模板库的匹配算法对复杂背景下的100幅黄瓜图像进行灰度模板匹配,实现黄瓜识别,正确识别率达87%,对颜色相近以及部分遮挡的果实仍能实现识别,并提出匹配的模板起始点作为黄瓜果实顶点。
4、研究了双目立体视觉原理、数学模型以及摄像机标定方法。构建双目视觉系统并进行摄像机标定实验,得到双目立体视觉系统的内外参数,根据双目视觉原理,对黄瓜采摘点进行定位计算,得到其空间坐标,利用FARO ARM三坐标测量仪测量采摘点实际空间坐标,并与计算结果进行比较,相对于物距500mm~750mm,实现最大定位误差9.46mm,可满足满足采摘系统要求。
5、建立了一套黄瓜采摘机器人识别和定位系统。构建黄瓜采摘机器人的硬件系统和软件系统,实现了在自然环境下采集的可见光图像中黄瓜果实的识别和空间定位。
Fruit and vegetable harvesting robot based on machine vision could identify fruits and vegetables to improve picking efficiency by using the ability of information apperceiving, but harvesting robot has not been widely used. One of the most important reasons is that the fruits recognition rate is not high enough. Thus there would be important practical significance for accelerating the practical process on agricultural harvesting robot and changing the current status of the main rely on manual harvesting by studying the target recognition of harvesting robot.
In this thesis, cucumber in greenhouse was taken as the study object, in order to solve the problem on recognition between cucumber fruits and leaves or stem which have similar color, the visible light images of cucumber in natural environment was studied. The fruit could be identified correctly by using template matching which using shape features of cucumber, the recognition rate could achieve to 87%, the three-dimensional coordinate location of cucumber picking point could be calculated by using binocular stereo vision, the location error could achieved to 9.46mm. The main research contents and results in the thesis are as follows:
1. The color features of cucumber image were studied. The R, G, B and H, S, I component of cucumber fruit and leaf was analyzed, although there is some differences between fruit and leaf on G component but this difference is not enough identify the fruit from the background which could achieve ideal cucumber segmentation. An identification method based on cucumber fruit shape features was proposed. The cucumber fruit border was obtained by using basic image processing on a simple background cucumber image, including preprocessing, morphological operations, region labeling and edge detection.
2. Cucumber fruit shape representation and description methods were discussed. 200 pieces of different cucumbers were taken as the sample source, the cucumber images were taken and the fruit border was obtained by image processing. Then the boundary was described by using elliptic Fourier description and was normalized. The average elliptic Fourier descriptors waer obtained by averaging all the descriptors. After that the standard cucumber was obtained by inverse transform of boundary descriptors.
3. Template deformation and template matching method was proposed. After 5 times of scale transformation and 13 times of angle transformation on the standard boundary, 65 pieces of cucumber shapes with different size and different angle was achieved. The gray template matching was done on 100 pieces of cucumber image with complex background by using template matching algorithm based on template database, and the correct recognition rate is 87%. The fruit with partial occlusion and similar color can still be recognized correctly. The matching template starting point was taken as the fruit top point.
4. The principle, mathematical models and calibration methods of binocular stereo vision was discussed. A binocular vision system was established. The internal and external parameters of the binocular stereo vision system were obtained by camera calibration experiments. And the space coordinates of cucumber picking point was calculated using triangulation measurement method, and the actual position of picking point was measured by using FARO ARM, and the results were compared with the calculation results, the results showed that the max positioning error is 9.46mm relative to the object distance between 500mm and 750mm which could meet the requirement of the vision system..
5. The identification and positioning system for cucumber harvesting robot was established including the hardware and software systems which achieve the identification of cucumber fruits and space positioning.
引文
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