棉花收获机器人视觉系统的研究
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摘要
我国是一个农业大国,棉花作为一种重要的经济作物,种植广泛,尤以新疆棉区最为明显。目前,我国棉花收获主要以手工采摘为主,机械收获为辅,棉花收获尚未完全实现机械化。而发达国家农业生产过程基本上实现机械化,并在信息技术、计算机技术和控制技术的推动下,向自动化、智能化方向发展,农业生产正在走向精准、高效之路。作为实现农业现代化的一种手段-农业机器人的研究受到各国的重视,作为移动式采摘机器人主要解决3方面的问题:①机器人本体结构设计,②机器人的自主导航,③目标的识别、定位与采摘。视觉系统作为实现目标的识别与定位的方式,显得尤为重要。目前,发达国家在农业生产自动化方面已开始应用机器视觉技术。我国农业机器人的研究刚刚起步,与发达国家相比还有很大的差距。因此抓住世界农业科技革命的机遇,及时研究以农业机器人为代表的新一代农业机械,对促进我国农业机械的自动化、智能化发展有着重要的意义。
本研究以田间采摘期的成熟棉花为研究对象,以棉桃的识别与定位为研究目标,结合计算机视觉,对棉花收获机器人的视觉系统进行研究。仿真和试验结果证明采用机器人进行棉花收获在理论和实际中是可行的。主要研究内容包括:
(1)针对农田环境的成熟棉花,首先对棉桃、棉叶和棉枝在6种不同的颜色空间:RGB,归一化rgb,HIS,YCrCb,I_1I_2I_3和L~*a~*b~*进行分析,得到适宜的分割空间。在此基础上,分析了棉桃、棉叶和棉枝颜色分布的差异,利用此差异,提出采用(R-B)颜色差值进行分割的模型。为进一步获得所识别的目标,采用两种方法进行识别:一种结合最大类间方差法和Freeman编码法,设计了一种动态链码对目标进行自动识别,识别准确率可达86%以上;另一种采用BP神经网络,对分割后的图像进行特征提取,并对特征进行归一化,通过试验建立了4-8-1的网络模型,其识别准确率可达到83%以上。
(2)基于小孔成像模型,对摄像机模型进行分析和标定。摄像机标定过程中,利用最小二乘法标定了摄像机的内外参数。标定结果表明在水平方向上标定误差为3.2象素,在垂直方向上标定误差较大,为8.7象素,并针对标定结果开发图像畸变纠正程序,对图像进行纠正。结果表明此纠正方法是有效的。
(3)分析了双目立体视觉的测量原理,对两种常见的摄像机摆放方式:会聚式和平行式进行分析。结合本实验室已有的试验条件,选用平行式的双目立体视觉,并在此基础上,通过实验确定两摄像机的合理基线距离B=80mm和合理测量深度范围z=[150,1000]mm之间。在此条件下,其测量误差可控制在10mm以内,能够满足机械手作业要求。
(4)分析了当前立体视觉中匹配的三种方法:基于区域、特征和相位的匹配,介绍了匹配中的相关概念和约束规则。依据本课题研究的目标,提出在外极线约束规则下,利用形心特征和链码特征相结合,通过相似度函数的判断进行立体视觉中的匹配。实验表明本算法是有效的,其匹配准确率可以达到86%以上。
(5)针对被噪声污染的棉花图像,提出基于因子图的小波域隐Markov树模型的去噪方法,该方法不仅利用了图像小波域尺度内系数的关联,而且反映了图像空域非平稳特性。实验结果表明,与其他去噪方法相比,该去噪方法的去噪图像峰值信噪比性能和视觉质量都有明显的提高。
Cotton, as an important cash crop, is widely planted in China, especially in Xinjiang district. At present, cotton is harvested mainly by manual and secondarily by machinery in China. However, with the impetus of technology, such as information, computer and automation, agricultural mechanization has completely been achieved in developed countries, and it has been changing to automatization and intelligence, where agriculture is on the way of achieving precise and efficient production. Agricultural robot, a means of achieving agricultural modernization, is being emphastically researched in many countries. Research on mobile harvesting robot mainly includes three parts: design of body structure, autonomous navigation and recognition, location and harvesting of the objects. Vision system, a means of achieving recognition and location of the objects, is especially important. At present, the technology of machine vision has been applied in agricultural automation in many developed countries. However, research on agricultural robot in China is at the beginning, and there is still a big step behind developed countries. Hence, the research of agricultural robot that is a representive of the new agricultural machinery is of significance to hold the opportunity of world scientific and technological revolution in agriculture and promote the development of automation, intelligence of agricultural machinery of China.
The objective of this research was to study vision system of cotton harvesting robot. The ripe cotton in the natural outdoors scene was the research object and the recognition and location of cotton fruits were the aims. Simulation and experimental results indicated that harvesting cotton by robot was feasible in theory and practice. Several main points in our research were as follows:
1. The color data of cotton fruits, cotton leaves and stems in six color space: RGB, normalized rgb, HIS, YCrCb, I_1I_2I_3 and L~*a~*b~* of the ripe cotton in the natural outdoors scene were analyzed, and RGB color space was considered to be an appropriate space for segmentation. Based on this, the differences of color distribution of cotton fruits, cotton leaves and stems were extendedly analyzed, and (R-B) subtraction module could provide with the best segmentation results. The methods of recognizing cotton fruits were researched, which were based on the combination of maximum Classes Square Error (Ostu) and Freeman chain coding and Back Propagation (BP) neural network respectively. On the combination of Ostu and Freeman chain coding, a new dynamic Freeman chain coding was designed for automatically recognizing the cotton fruits. And the 4-8-1 structure of BP neural network was designed. The accuracy ratio of recognition reached to 86% and 83%, respectively.
2. The model of camera was discussed and the results of calibration of the camera were provided. The intrinsic and extrinsic parameters were obtained by the least square method with the pinhole model. The result of calibration showed there were 3.2 pixels errors on horizon and 8.7 pixels in vertical. Based on the parameters of calibration, the method of image rectifying was developed and the distortion of images was rectified by this method. The result showed the method was effective.
3. The theory of stereo vision and two models of camera emplacement which were convergent and parallel model were analyzed respectively. The parallel model of stereo vision was selected according to the existing devices of lab. The appropriate measuring distance between cameras was 80mm and the appropriate measuring distance range was from 150mm to 1000mm. Under this condition, the measure error could be controlled within 10mm which was satisfied with the requirements of the manipulator.
4. Three matching algorithms based on area, feature and phase often used in the stereo vision were analyzed. And the related definitions and constrained rules used in the algorithms were introduced respectively. With the constraint of the epipole line, with the characteristics of shape gravity and chain coding, the match algorithm of stereo vision was made by the similarity function. Experimental results showed this algorithm was effective and the accuracy ratio of matching reached higher 86%.
5. A wavelet field hidden Markov tree structure model based on factor graph was proposed to denoise corrupted cotton images. The model not only used the intra-scale relation of image wavelet field, but also considered the unstationary characteristics of image space field. Simulation results suggested, compared with other image denoising methods, the proposed method had outstanding performance on signal-to-noise ratio and good quality of vision.
本研究以田间采摘期的成熟棉花为研究对象,以棉桃的识别与定位为研究目标,结合计算机视觉,对棉花收获机器人的视觉系统进行研究。仿真和试验结果证明采用机器人进行棉花收获在理论和实际中是可行的。主要研究内容包括:
(1)针对农田环境的成熟棉花,首先对棉桃、棉叶和棉枝在6种不同的颜色空间:RGB,归一化rgb,HIS,YCrCb,I_1I_2I_3和L~*a~*b~*进行分析,得到适宜的分割空间。在此基础上,分析了棉桃、棉叶和棉枝颜色分布的差异,利用此差异,提出采用(R-B)颜色差值进行分割的模型。为进一步获得所识别的目标,采用两种方法进行识别:一种结合最大类间方差法和Freeman编码法,设计了一种动态链码对目标进行自动识别,识别准确率可达86%以上;另一种采用BP神经网络,对分割后的图像进行特征提取,并对特征进行归一化,通过试验建立了4-8-1的网络模型,其识别准确率可达到83%以上。
(2)基于小孔成像模型,对摄像机模型进行分析和标定。摄像机标定过程中,利用最小二乘法标定了摄像机的内外参数。标定结果表明在水平方向上标定误差为3.2象素,在垂直方向上标定误差较大,为8.7象素,并针对标定结果开发图像畸变纠正程序,对图像进行纠正。结果表明此纠正方法是有效的。
(3)分析了双目立体视觉的测量原理,对两种常见的摄像机摆放方式:会聚式和平行式进行分析。结合本实验室已有的试验条件,选用平行式的双目立体视觉,并在此基础上,通过实验确定两摄像机的合理基线距离B=80mm和合理测量深度范围z=[150,1000]mm之间。在此条件下,其测量误差可控制在10mm以内,能够满足机械手作业要求。
(4)分析了当前立体视觉中匹配的三种方法:基于区域、特征和相位的匹配,介绍了匹配中的相关概念和约束规则。依据本课题研究的目标,提出在外极线约束规则下,利用形心特征和链码特征相结合,通过相似度函数的判断进行立体视觉中的匹配。实验表明本算法是有效的,其匹配准确率可以达到86%以上。
(5)针对被噪声污染的棉花图像,提出基于因子图的小波域隐Markov树模型的去噪方法,该方法不仅利用了图像小波域尺度内系数的关联,而且反映了图像空域非平稳特性。实验结果表明,与其他去噪方法相比,该去噪方法的去噪图像峰值信噪比性能和视觉质量都有明显的提高。
Cotton, as an important cash crop, is widely planted in China, especially in Xinjiang district. At present, cotton is harvested mainly by manual and secondarily by machinery in China. However, with the impetus of technology, such as information, computer and automation, agricultural mechanization has completely been achieved in developed countries, and it has been changing to automatization and intelligence, where agriculture is on the way of achieving precise and efficient production. Agricultural robot, a means of achieving agricultural modernization, is being emphastically researched in many countries. Research on mobile harvesting robot mainly includes three parts: design of body structure, autonomous navigation and recognition, location and harvesting of the objects. Vision system, a means of achieving recognition and location of the objects, is especially important. At present, the technology of machine vision has been applied in agricultural automation in many developed countries. However, research on agricultural robot in China is at the beginning, and there is still a big step behind developed countries. Hence, the research of agricultural robot that is a representive of the new agricultural machinery is of significance to hold the opportunity of world scientific and technological revolution in agriculture and promote the development of automation, intelligence of agricultural machinery of China.
The objective of this research was to study vision system of cotton harvesting robot. The ripe cotton in the natural outdoors scene was the research object and the recognition and location of cotton fruits were the aims. Simulation and experimental results indicated that harvesting cotton by robot was feasible in theory and practice. Several main points in our research were as follows:
1. The color data of cotton fruits, cotton leaves and stems in six color space: RGB, normalized rgb, HIS, YCrCb, I_1I_2I_3 and L~*a~*b~* of the ripe cotton in the natural outdoors scene were analyzed, and RGB color space was considered to be an appropriate space for segmentation. Based on this, the differences of color distribution of cotton fruits, cotton leaves and stems were extendedly analyzed, and (R-B) subtraction module could provide with the best segmentation results. The methods of recognizing cotton fruits were researched, which were based on the combination of maximum Classes Square Error (Ostu) and Freeman chain coding and Back Propagation (BP) neural network respectively. On the combination of Ostu and Freeman chain coding, a new dynamic Freeman chain coding was designed for automatically recognizing the cotton fruits. And the 4-8-1 structure of BP neural network was designed. The accuracy ratio of recognition reached to 86% and 83%, respectively.
2. The model of camera was discussed and the results of calibration of the camera were provided. The intrinsic and extrinsic parameters were obtained by the least square method with the pinhole model. The result of calibration showed there were 3.2 pixels errors on horizon and 8.7 pixels in vertical. Based on the parameters of calibration, the method of image rectifying was developed and the distortion of images was rectified by this method. The result showed the method was effective.
3. The theory of stereo vision and two models of camera emplacement which were convergent and parallel model were analyzed respectively. The parallel model of stereo vision was selected according to the existing devices of lab. The appropriate measuring distance between cameras was 80mm and the appropriate measuring distance range was from 150mm to 1000mm. Under this condition, the measure error could be controlled within 10mm which was satisfied with the requirements of the manipulator.
4. Three matching algorithms based on area, feature and phase often used in the stereo vision were analyzed. And the related definitions and constrained rules used in the algorithms were introduced respectively. With the constraint of the epipole line, with the characteristics of shape gravity and chain coding, the match algorithm of stereo vision was made by the similarity function. Experimental results showed this algorithm was effective and the accuracy ratio of matching reached higher 86%.
5. A wavelet field hidden Markov tree structure model based on factor graph was proposed to denoise corrupted cotton images. The model not only used the intra-scale relation of image wavelet field, but also considered the unstationary characteristics of image space field. Simulation results suggested, compared with other image denoising methods, the proposed method had outstanding performance on signal-to-noise ratio and good quality of vision.
引文
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