基于嵌入式机器视觉的信息采集与处理技术研究
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摘要
现有机器视觉设备不太方便携带到田间作业,且价格昂贵,导致该项技术不易向农户推广。嵌入式机器视觉系统是以嵌入式计算机为应用中心,以机器视觉技术为理论基础,软硬件可裁剪,是机器视觉技术的扩展与延伸。研究嵌入式机器视觉的作物生长信息采集与处理技术,能促进该项技术在农、林、牧、渔等互联网和供电不方便的野外应用,实现信息快速获取,使研究工作显得更有现实意义。
本文结合机器视觉系统的结构,开发了嵌入式机器视觉设备。全文围绕嵌入式机器视觉设备的研发及其在作物生长信息的采集与处理过程中的适用性展开了研究,选取了水果和植物叶片进行了信息采集与处理,对反映作物生长的大小、面积、产量等信息采集与处理算法进行了适用性研究,取得了较好的效果。主要结论如下：
(1)对提高图像处理运行速度算法进行了研究。提出的将多维数组合理降为一维数组方法,可数倍的节省内存运行空间,提高程序执行效率；使用整数系数代替浮点运算、将乘除法换成移位运算、把四字节的数据类型转化为两字节、使用查表算法代替程序中的乘法运算、编写自己的代码替代开发环境提供的API函数等方式来提高图像处理算法的运行效率。从图像灰度化的实验结果来看,采用研究的方法可提高软件运行速度20倍以上。该方法具有很高的灵活性和实用价值。
(2)研制了Linux与Android系统的嵌入式机器视觉设备。该设备使用核心为嵌入式计算机的手机开发模块代替计算机视觉系统中的计算机和摄像头部分,实现图像采集和数据处理；利用倾角测量技术控制摄像头与研究对象之间的角度；采用距离测量技术控制摄像头与研究对象之间的距离；采用Java语言为该设备和市场上流行的Android系统智能手机I9300开发了图像处理软件。实验结果表明,该视觉系统可测量不同形状和颜色的叶片面积及对柑橘进行估产,延伸了机器视觉技术的应用。
(3)应用所研制的嵌入式机器视觉系统,对簇生水果的数量判别进行了研究。提出了改进Freeman八邻域链码对簇生水果数量判别的方法,在Freeman八邻域链码的基础上,增加了3个新的元素,即“S”、“8”、“9”,其中“S”是簇生区域水果图像轮廓链码的起点,“8”是图像轮廓的方向变化转折点,“9”是图像轮廓的最低点；对图像轮廓编码完成后,利用链码中“8”的个数信息进行簇生水果的数量判别。利用该方法对采摘前两周的赣南脐橙和烟台苹果进行了判别,实验结果表明,该方法能100%的判别出二、三、四簇生情况,对五簇生的正确识别率能达到60%以上,但比较困难正确判断六簇生以上的情况。该方法可以借鉴判断具有规则轮廓水果的簇生情况,如梨、西红柿等,对提高采摘前水果估产准确率具有理论和实际意义。
(4)通过所研制的嵌入式机器视觉设备对采摘前两周的单株柑橘树进行估产研究。提出了一种基于颜色信息的半自动自适应分割算法,对果园现场拍摄的柑橘树进行了柑橘分割；结合形态学滤波器,对被判别为具有簇生情况的图像区域进行了边缘平滑；采用改进的Freeman八邻域链码对簇生区域水果数量进行了判别；实现柑橘数量的估计。对赣南脐橙单株果树进行了估产,结果显示能达到近90%的准确率。为果园规划产前产量分布图提供了技术支持,为进一步估产提供技术方案。
(5)应用所研制的嵌入式机器视觉设备,对叶面积测量的算法进行了研究。提出了一种适合嵌入式手持设备操作特性的半自动自适应分割算法,通过人工在触摸屏上显示的叶片图像的周围任意画个封闭圈,采用Canny边缘检测算法获取叶片的边缘像素位置信息,利用中值滤波对图像去噪处理,采用Otsu分割算法对封闭圈内的研究对象进行分割并二值化,图像重构以后把边缘检测的像素位置插入图像中,且对研究边缘内的像素进行填充。该方法保证了研究对象边缘的完整性的同时消除了图像中由光照引起的白噪声,提高了测量结果的精度,实验结果显示测量精度能达到1%,表明本研究方法具有实用性。
The existing machine vision systems are inconvenient to carry to the field for work. The expensive price and complex operation restrict it from popularization to the farmers. The embedded machine vision technology is a kind of machine vision technology using the embedded computer as data processing, and its software and hardware can be cut according to its practical application. It is the expansion and extension of the machine vision technology. Study of the information acquisition and processing technology based on embedded system computer vision has practical application in agriculture, forestry, animal husbandry and fishery which are far away from cities with limited Internet service and power supply. Thus, related research of machine vision technology in handheld devices has practical sense.
In this context, a handheld machine vision system was developed using the machine vision technology. The main researches were focused on the development of handheld machine vision system and the test of its adaptability in crop growth information collection and data process. Fruits, plant leaves and citrus were used as experimental subjects to evaluate the adaptability of the algorithms to calculate the size of fruits, the area of leaves and the yield of citrus, and good results were obtained in this research. The main conclusions are as follows:
(1) The developed handheld device vision system was adopted to study the processing algorithm of the handheld device to achieve higher speed. The procedures include dimension reduction for array, usage of integer arithmetic to simulate floating point, combination with computer principle, algorithm transmission from multiplication and division to shift operator, rational definition of data type in program, algorithm transmission from multiplication and division to table lookup, application of self-developed algorithm instead of that provided by development environment. According to the results of image grizzled processing, using this methods as mentioned above makes a significant improvement on the speed of software with more than20times. The methods were high flexibility and practical value.
(2) Two handheld machine vision systems were developed. This system used mobile phone development module to replace the computer in computer vision system to set up a handheld machine vision system for data process. The inclination measurement module was used to measure the angle between the camera and the target. The distance measurement module was used to measure the distance between the camera and the target. The other one was Android based smart cellphone19300. The application softwares integrated with machine vision were written in Java. The app's algorithm applicability verified that the portable vision technology possesses stability and repeatability. All above extends application of computer vision in agriculture.
(3) The developed handheld device vision system was performed to discriminant the quantity of cluster fruits. The paper introduces an improved Freeman chain code to discriminant the number of fruits. It adds three new elements namely "S'、"8"、"9", while the "S" is the starting point of clustering area edge,"8" is the turning point, and "9" is the lowest point of image contour. After the coding of edge image, the number of "8" is used for the discrimination of fruit number in clustering area. When the method is tested on Gannan orange and Yantai apple,100%discriminant rate is accomplished in two, three and four clustering case, in five clustering case can reaches above60%, but it is very difficult to correctly discriminant six clustering case. This method can extend to other clustering cases in fruit that has regular contour, like pear, tomato etc. and has theoretical and practical meaning in enhancing pre-harvesting fruit yield estimation.
(4) The developed handheld device vision system was applied to estimate the citrus yield of a single tree two weeks before harvest. A semi-automatic self-adjusted segmentation algorithm was promoted for on-site application. It utilizes morphology filter for edge smoothing in clustering area, then improved Freeman8neighborhoods for clustering area fruit number counting calculates the total number of citrus. This algorithm has been used for Ganan orange yield estimation and reaches90%accurate rate. This method facilitates orchard pre-harvesting yield distribution mapping, and provides technical solution for further yield estimation.
(5) The developed handheld device vision system was used to research on the vision algorithm for information collection and processing methods of crop growth. In this research, we put forward a semi-automatic adaptive segmentation algorithm for handheld device. During the application of leaf area measurement, after artificial selection of leaves to be measured, the boundary of the leaf image displayed on the touch screen is drawn as a closed circle. And then, Canny edge detection algorithm is used to obtain the information of the edge pixel position of the leaf followed by median filtering and binarization (using Otsu threshold algorithm). After the image reconstruction, the edge pixel would be inserted into the image, and the pixels within the edges would be filled. Results show that the display error is within1%which means this method ensures the integrity of the research object edge while eliminating the white noise in the image to improve the accuracy of the measurement results.
本文结合机器视觉系统的结构,开发了嵌入式机器视觉设备。全文围绕嵌入式机器视觉设备的研发及其在作物生长信息的采集与处理过程中的适用性展开了研究,选取了水果和植物叶片进行了信息采集与处理,对反映作物生长的大小、面积、产量等信息采集与处理算法进行了适用性研究,取得了较好的效果。主要结论如下：
(1)对提高图像处理运行速度算法进行了研究。提出的将多维数组合理降为一维数组方法,可数倍的节省内存运行空间,提高程序执行效率；使用整数系数代替浮点运算、将乘除法换成移位运算、把四字节的数据类型转化为两字节、使用查表算法代替程序中的乘法运算、编写自己的代码替代开发环境提供的API函数等方式来提高图像处理算法的运行效率。从图像灰度化的实验结果来看,采用研究的方法可提高软件运行速度20倍以上。该方法具有很高的灵活性和实用价值。
(2)研制了Linux与Android系统的嵌入式机器视觉设备。该设备使用核心为嵌入式计算机的手机开发模块代替计算机视觉系统中的计算机和摄像头部分,实现图像采集和数据处理；利用倾角测量技术控制摄像头与研究对象之间的角度；采用距离测量技术控制摄像头与研究对象之间的距离；采用Java语言为该设备和市场上流行的Android系统智能手机I9300开发了图像处理软件。实验结果表明,该视觉系统可测量不同形状和颜色的叶片面积及对柑橘进行估产,延伸了机器视觉技术的应用。
(3)应用所研制的嵌入式机器视觉系统,对簇生水果的数量判别进行了研究。提出了改进Freeman八邻域链码对簇生水果数量判别的方法,在Freeman八邻域链码的基础上,增加了3个新的元素,即“S”、“8”、“9”,其中“S”是簇生区域水果图像轮廓链码的起点,“8”是图像轮廓的方向变化转折点,“9”是图像轮廓的最低点；对图像轮廓编码完成后,利用链码中“8”的个数信息进行簇生水果的数量判别。利用该方法对采摘前两周的赣南脐橙和烟台苹果进行了判别,实验结果表明,该方法能100%的判别出二、三、四簇生情况,对五簇生的正确识别率能达到60%以上,但比较困难正确判断六簇生以上的情况。该方法可以借鉴判断具有规则轮廓水果的簇生情况,如梨、西红柿等,对提高采摘前水果估产准确率具有理论和实际意义。
(4)通过所研制的嵌入式机器视觉设备对采摘前两周的单株柑橘树进行估产研究。提出了一种基于颜色信息的半自动自适应分割算法,对果园现场拍摄的柑橘树进行了柑橘分割；结合形态学滤波器,对被判别为具有簇生情况的图像区域进行了边缘平滑；采用改进的Freeman八邻域链码对簇生区域水果数量进行了判别；实现柑橘数量的估计。对赣南脐橙单株果树进行了估产,结果显示能达到近90%的准确率。为果园规划产前产量分布图提供了技术支持,为进一步估产提供技术方案。
(5)应用所研制的嵌入式机器视觉设备,对叶面积测量的算法进行了研究。提出了一种适合嵌入式手持设备操作特性的半自动自适应分割算法,通过人工在触摸屏上显示的叶片图像的周围任意画个封闭圈,采用Canny边缘检测算法获取叶片的边缘像素位置信息,利用中值滤波对图像去噪处理,采用Otsu分割算法对封闭圈内的研究对象进行分割并二值化,图像重构以后把边缘检测的像素位置插入图像中,且对研究边缘内的像素进行填充。该方法保证了研究对象边缘的完整性的同时消除了图像中由光照引起的白噪声,提高了测量结果的精度,实验结果显示测量精度能达到1%,表明本研究方法具有实用性。
The existing machine vision systems are inconvenient to carry to the field for work. The expensive price and complex operation restrict it from popularization to the farmers. The embedded machine vision technology is a kind of machine vision technology using the embedded computer as data processing, and its software and hardware can be cut according to its practical application. It is the expansion and extension of the machine vision technology. Study of the information acquisition and processing technology based on embedded system computer vision has practical application in agriculture, forestry, animal husbandry and fishery which are far away from cities with limited Internet service and power supply. Thus, related research of machine vision technology in handheld devices has practical sense.
In this context, a handheld machine vision system was developed using the machine vision technology. The main researches were focused on the development of handheld machine vision system and the test of its adaptability in crop growth information collection and data process. Fruits, plant leaves and citrus were used as experimental subjects to evaluate the adaptability of the algorithms to calculate the size of fruits, the area of leaves and the yield of citrus, and good results were obtained in this research. The main conclusions are as follows:
(1) The developed handheld device vision system was adopted to study the processing algorithm of the handheld device to achieve higher speed. The procedures include dimension reduction for array, usage of integer arithmetic to simulate floating point, combination with computer principle, algorithm transmission from multiplication and division to shift operator, rational definition of data type in program, algorithm transmission from multiplication and division to table lookup, application of self-developed algorithm instead of that provided by development environment. According to the results of image grizzled processing, using this methods as mentioned above makes a significant improvement on the speed of software with more than20times. The methods were high flexibility and practical value.
(2) Two handheld machine vision systems were developed. This system used mobile phone development module to replace the computer in computer vision system to set up a handheld machine vision system for data process. The inclination measurement module was used to measure the angle between the camera and the target. The distance measurement module was used to measure the distance between the camera and the target. The other one was Android based smart cellphone19300. The application softwares integrated with machine vision were written in Java. The app's algorithm applicability verified that the portable vision technology possesses stability and repeatability. All above extends application of computer vision in agriculture.
(3) The developed handheld device vision system was performed to discriminant the quantity of cluster fruits. The paper introduces an improved Freeman chain code to discriminant the number of fruits. It adds three new elements namely "S'、"8"、"9", while the "S" is the starting point of clustering area edge,"8" is the turning point, and "9" is the lowest point of image contour. After the coding of edge image, the number of "8" is used for the discrimination of fruit number in clustering area. When the method is tested on Gannan orange and Yantai apple,100%discriminant rate is accomplished in two, three and four clustering case, in five clustering case can reaches above60%, but it is very difficult to correctly discriminant six clustering case. This method can extend to other clustering cases in fruit that has regular contour, like pear, tomato etc. and has theoretical and practical meaning in enhancing pre-harvesting fruit yield estimation.
(4) The developed handheld device vision system was applied to estimate the citrus yield of a single tree two weeks before harvest. A semi-automatic self-adjusted segmentation algorithm was promoted for on-site application. It utilizes morphology filter for edge smoothing in clustering area, then improved Freeman8neighborhoods for clustering area fruit number counting calculates the total number of citrus. This algorithm has been used for Ganan orange yield estimation and reaches90%accurate rate. This method facilitates orchard pre-harvesting yield distribution mapping, and provides technical solution for further yield estimation.
(5) The developed handheld device vision system was used to research on the vision algorithm for information collection and processing methods of crop growth. In this research, we put forward a semi-automatic adaptive segmentation algorithm for handheld device. During the application of leaf area measurement, after artificial selection of leaves to be measured, the boundary of the leaf image displayed on the touch screen is drawn as a closed circle. And then, Canny edge detection algorithm is used to obtain the information of the edge pixel position of the leaf followed by median filtering and binarization (using Otsu threshold algorithm). After the image reconstruction, the edge pixel would be inserted into the image, and the pixels within the edges would be filled. Results show that the display error is within1%which means this method ensures the integrity of the research object edge while eliminating the white noise in the image to improve the accuracy of the measurement results.
引文
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