CN103116747A - Method and system for automatically recognizing images of stalks and leaves of corns - Google Patents
Method and system for automatically recognizing images of stalks and leaves of corns Download PDFInfo
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Abstract
Description
技术领域technical field
本发明涉及图像处理技术领域,尤其涉及一种玉米茎秆识别方法。The invention relates to the technical field of image processing, in particular to a method for identifying corn stalks.
背景技术Background technique
在玉米施肥作业过程中,自动施肥机上的施肥装置会与玉米的茎秆等“障碍物”发生碰撞,导致设备损坏或伤害作物,因此需要对这些“障碍物”进行实时、准确的识别和定位,使得自动施肥机在可能与“障碍物”发生碰撞之前改变移动路线或停止移动。During the corn fertilization process, the fertilization device on the automatic fertilizer spreader will collide with "obstacles" such as corn stalks, resulting in equipment damage or injury to crops. Therefore, real-time and accurate identification and positioning of these "obstacles" is required , causing the automatic fertilizer applicator to change its movement route or stop before it may collide with the "obstacle".
这种识别和定位技术能够通过机器视觉来实现,采用摄像机采集图像,利用计算机、DSP芯片等设备对图像进行处理。现有技术中采用对玉米茎秆的色差R-B图像进行hough变换直线检测进而实现茎秆的识别,虽然基于Hough变换的直线检测方法是一种日趋成熟的算法,但计算量比较大,不利于实时应用和变换过程中丢失了线段的端点和长度信息。This identification and positioning technology can be realized through machine vision, using cameras to collect images, and using computers, DSP chips and other equipment to process the images. In the prior art, the hough transform line detection method is used to detect the color difference R-B image of corn stalks to realize the recognition of the stalks. Although the line detection method based on Hough transform is a mature algorithm, the calculation amount is relatively large, which is not conducive to real-time The endpoint and length information of the line segment is lost during the application and transformation process.
发明内容Contents of the invention
本发明要解决的技术问题是:提供一种玉米茎秆识别方法,其能够实时、准确地识别出玉米的茎秆,并能使自动施肥机在施肥作业时避免因茎秆的阻碍造成设备损坏或伤害玉米植株等问题的发生。The technical problem to be solved by the present invention is to provide a method for identifying corn stalks, which can identify corn stalks in real time and accurately, and enable the automatic fertilizer applicator to avoid equipment damage due to the obstruction of the stalks during fertilization operations Or damage to corn plants and other problems.
为解决上述问题,本发明提供了一种玉米识别方法,包括以下步骤:In order to solve the above problems, the invention provides a method for identifying corn, comprising the following steps:
(1)采集RGB彩色图像;(1) Collect RGB color images;
(2)将所述RGB彩色图像转化为灰度图像;(2) converting the RGB color image into a grayscale image;
(3)对所述灰度图像进行预处理;(3) Preprocessing the grayscale image;
(4)通过二值化处理将预处理后的所述灰度图像转化为二值化图像;(4) converting the preprocessed grayscale image into a binarized image through binarization processing;
(5)对所述二值化图像进行直线检测;(5) performing line detection on the binarized image;
(6)计算所述二值化图像被检测得到的直线分割的不同区域的形状特征,并根据所述不同区域的形状特征对采集到的RGB彩色图像进行识别,得到最终的茎秆目标。(6) Calculate the shape features of the different regions of the binarized image segmented by the detected line, and identify the collected RGB color images according to the shape features of the different regions to obtain the final stalk target.
所述预处理为小波提升处理,其能够提取灰度图像低频轮廓信息、抑制高频噪声。The preprocessing is wavelet lifting processing, which can extract low-frequency contour information of the gray image and suppress high-frequency noise.
当步骤(3)中的预处理为小波提升处理时,步骤(4)进一步包括:When the preprocessing in step (3) is wavelet lifting processing, step (4) further includes:
对经过小波提升处理后的像素降低后的灰度图像采用微分算子进行二值化处理,将灰度图像转化为二值化图像。Differential operator is used to binarize the grayscale image after wavelet lifting processing and pixel reduction, and the grayscale image is converted into a binary image.
步骤(5)进一步包括:Step (5) further includes:
利用过零点检测获得二值化图像的边界点,进而通过边界跟踪算法将边界点转化为链码表示的符号信息,基于边界跟踪得到的链码串进行直线检测。The boundary points of the binarized image are obtained by zero-crossing detection, and then the boundary points are converted into symbol information represented by chain codes through the boundary tracking algorithm, and the line detection is performed based on the chain code string obtained by boundary tracking.
本发明通过对玉米茎秆的RGB彩色图像进行灰度化、小波提升、二值化处理后,通过边界点检测,利用基于链码的直线检测进而能够实时、准确地识别出玉米的茎秆,使自动施肥机在施肥作业时避免因茎秆的阻碍造成设备损坏或伤害玉米植株等问题的发生。In the present invention, after the RGB color image of the corn stalk is grayscaled, wavelet lifted, and binarized, the boundary point detection is performed, and the straight line detection based on the chain code is used to identify the corn stalk in real time and accurately. Make the automatic fertilizer applicator avoid problems such as equipment damage or damage to corn plants due to the obstruction of the stalk during fertilization.
附图说明Description of drawings
图1为本发明实施方式中所述玉米茎秆识别方法的流程图;Fig. 1 is the flowchart of the corn stalk identification method described in the embodiment of the present invention;
图2为8方向链码的示意图。FIG. 2 is a schematic diagram of an 8-direction chain code.
具体实施方式Detailed ways
下面结合附图和实施例,对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明,但不用来限制本发明的范围。The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
如图1所示,本发明所述的一种玉米茎秆识别方法,包括以下步骤:As shown in Figure 1, a kind of corn stalk identification method of the present invention comprises the following steps:
(1)采集RGB彩色图像;(1) Collect RGB color images;
(2)将所述RGB彩色图像转化为灰度图像;(2) converting the RGB color image into a grayscale image;
本步骤中,对采集到的RGB彩色图像进行灰度化处理,将彩色图像转化为灰度图像。设彩色图像中原来某点的颜色为RGB(R,G,B),可以通过下面几种方法,将其转换为灰度:In this step, grayscale processing is performed on the collected RGB color image, and the color image is converted into a grayscale image. Let the original color of a point in the color image be RGB(R,G,B), and it can be converted to grayscale by the following methods:
1)浮点算法:Gray=R*0.3+G*0.59+B*0.11;1) Floating point algorithm: Gray=R*0.3+G*0.59+B*0.11;
2)整数方法:Gray=(R*30+G*59+B*11)/100;2) Integer method: Gray=(R*30+G*59+B*11)/100;
3)移位方法:Gray=(R*76+G*151+B*28)>>8;3) Shift method: Gray=(R*76+G*151+B*28)>>8;
4)平均值法:Gray=(R+G+B)/3;4) Average method: Gray=(R+G+B)/3;
5)仅取绿色:Gray=G;5) Take only green: Gray=G;
通过上述任一种方法求得Gray后,将原来的RGB(R,G,B)中的R、G、B统一用Gray替换,形成新的颜色RGB(Gray,Gray,Gray),用它替换原来的RGB(R,G,B)就得到了灰度图像。After obtaining Gray by any of the above methods, replace R, G, and B in the original RGB (R, G, B) with Gray to form a new color RGB (Gray, Gray, Gray), and use it to replace The original RGB (R, G, B) gets a grayscale image.
在图像处理过程中,为利用彩色图像的颜色特征将目标对象从图像背景中分割出来,经常对彩色图像进行降维处理,仅利用某一特征分量来描述图像的颜色,将彩色图像转化为灰度图像。与此同时,通过彩色图像的灰度化,也使图像数据量明显减少,大大提高了图像处理速度。本发明中,由于图像的色彩信息对所要提取的茎秆形态信息并不重要,灰度图像较好地保留了原始彩色图像中茎秆的形态信息。In the process of image processing, in order to use the color features of the color image to separate the target object from the image background, the color image is often subjected to dimensionality reduction processing, and only a certain feature component is used to describe the color of the image, and the color image is converted into a gray image. degree image. At the same time, the amount of image data is also significantly reduced through the grayscale of the color image, which greatly improves the image processing speed. In the present invention, since the color information of the image is not important to the morphological information of the stalk to be extracted, the grayscale image better retains the morphological information of the stalk in the original color image.
(3)对所述灰度图像进行预处理;(3) Preprocessing the grayscale image;
上述预处理为小波提升处理,对转化后的灰度图像进行小波提升处理,所述小波提升处理能够提取灰度图像低频轮廓信息、抑制高频噪声;Above-mentioned preprocessing is wavelet lifting processing, carries out wavelet lifting processing to the converted gray scale image, and described wavelet lifting processing can extract gray scale image low-frequency profile information, restrain high-frequency noise;
小波提升是一种更为快速有效的小波变换实现方法,被称为第二代小波变换。小波提升不依赖于傅里叶变换,继承了第一代小波的多分辨率的特征,小波变换后的系数是整数,无需额外的内存,可用本位操作进行运算,能够实现任意图像尺寸的小波变换。借助于因子化小波变换,所有小波变换都能够用小波提升模式来实现。Wavelet lifting is a faster and more efficient way to realize wavelet transform, which is called the second generation wavelet transform. Wavelet lifting does not depend on Fourier transform, and inherits the multi-resolution characteristics of the first generation wavelet. The coefficients after wavelet transform are integers, and no additional memory is required. Operations can be performed using local operations, and wavelet transform of any image size can be realized. . All wavelet transforms can be implemented with wavelet lifting modes by means of factorized wavelet transforms.
本步骤中,小波提升处理包括分裂、预测和更新,考虑信号sj={sj,l|0≤l≤2j},它经过一级小波变换后得到的低频信号为sj-1和高频信号dj-1,则构建低分辨率图像的具体实现过程为:In this step, the wavelet lifting process includes splitting, prediction and updating. Considering the signal s j ={s j,l |0≤l≤2 j }, the low-frequency signals obtained after one-level wavelet transformation are s j-1 and high-frequency signal d j-1 , the specific implementation process of constructing a low-resolution image is:
I.分裂:将输入信号sj分为奇、偶两个子集,位于偶下标位置的元素构成的集合记为evenj-1={sj,2l|0≤l≤2j-1-1},位于奇下标位置的元素构成的集合记为oddj-1={sj,2l+1|0≤l≤2j-1-1};I. Splitting: Divide the input signal s j into odd and even subsets, and the set of elements at the even subscript position is recorded as even j-1 ={s j,2l |0≤l≤2 j-1 -1} , the set of elements at odd subscript positions is recorded as odd j-1 ={s j,2l+1 |0≤l≤2 j-1 -1};
II.预测:在基于原始数据相关性的基础上,用偶数序列的预测值去预测或者内插奇数序列;II. Prediction: Based on the correlation of the original data, use the predicted value of the even sequence to predict or interpolate the odd sequence;
III.更新:更新的目的是要找一个更好的子集sj-1,使其保持原图的某一标量特性Q(x)(如均方根值不变),即Q(sj-1)=Q(sj);III. Update: The purpose of the update is to find a better subset s j-1 to keep a certain scalar property Q(x) of the original image (such as the root mean square value unchanged), that is, Q(s j-1 )=Q(s j );
其中,对于小波提升,存在分裂算子Split、预测算子P和更新算子U,使得Among them, for wavelet lifting, there are split operator Split, predictor P and update operator U, so that
(evenj-1,oddj-1)=Split(sj),(even j-1 ,odd j-1 )=Split(s j ),
dj-1=oddj-1-P(evenj-1),d j-1 = odd j-1 -P(even j-1 ),
sj-1=evenj-1+U(dj-1)。s j-1 =even j-1 +U(d j-1 ).
一幅像素为M×N的图像,经过一次小波提升后,取其低频分量构建低分辨率图像,其像素为此外,由于噪声大多分布在图像的高频部分,因此基于小波提升所构建的低分辨率图像有效地抑制了高频噪声。An image with a pixel size of M×N, after a wavelet upgrade, its low-frequency components are used to construct a low-resolution image, and its pixels are In addition, since the noise is mostly distributed in the high-frequency part of the image, the low-resolution image constructed based on wavelet lifting effectively suppresses the high-frequency noise.
(4)通过二值化处理将预处理后的所述灰度图像转化为二值化图像;(4) converting the preprocessed grayscale image into a binarized image through binarization processing;
当步骤(3)中的预处理为小波提升处理时,本步骤进一步包括:对经过小波提升处理后的像素降低后的灰度图像采用微分算子进行二值化处理,将灰度图像转化为二值化图像;When the preprocessing in step (3) is wavelet lifting processing, this step further includes: using a differential operator to perform binary processing on the grayscale image after wavelet lifting processing and pixel reduction, converting the grayscale image into binarized image;
(5)对所述二值化图像进行直线检测;(5) performing line detection on the binarized image;
本步骤进一步包括:This step further includes:
利用过零点检测获得二值化图像的边界点,进而通过边界跟踪算法将边界点转化为链码表示的符号信息,基于边界跟踪得到的链码串进行直线检测;The boundary points of the binarized image are obtained by zero-crossing detection, and then the boundary points are converted into symbol information represented by the chain code through the boundary tracking algorithm, and the line detection is performed based on the chain code string obtained by boundary tracking;
本步骤中,边界跟踪算法主要包括扫描过程和跟踪过程,假设边界存在于像素之间,由边元构成,分为水平和垂直两种,设当前考察点坐标为(x,y),判断是否存在水平边元以(x+1,y)为参考点,判断是否存在垂直边元以(x,y+1)为参考点,当考察点与参考点的值相同时,这两个点之间没有边界通过,边元的值为零,根据它们都是目标点还是背景点分为正零和负零两种,当考察点与参考点的值相异时两点之间有边界通过,边元的值非零,根据考察点是目标点还是背景点分别取正或负;In this step, the boundary tracking algorithm mainly includes the scanning process and the tracking process. It is assumed that the boundary exists between pixels and is composed of edge elements. There is a horizontal edge element with (x+1, y) as the reference point, and it is judged whether there is a vertical edge element with (x, y+1) as the reference point. There is no boundary passing between them, and the value of the edge element is zero. According to whether they are target points or background points, they are divided into two types: positive zero and negative zero. The value of the edge element is non-zero, and it is positive or negative according to whether the investigation point is a target point or a background point;
跟踪方向规定如下:目标点始终在前进方向的左边,对于一个连通区域的外边界,跟踪方向为逆时针方向,而内边界(内部孔洞所形成的边界)则为顺时针方向,当在封闭链码基础上求面积时,外边界链码所围成的区域面积为正,而内边界链码所围成区域而积为负;The tracking direction is stipulated as follows: the target point is always on the left of the forward direction. For the outer boundary of a connected area, the tracking direction is counterclockwise, while the inner boundary (the boundary formed by the inner hole) is clockwise. When in a closed chain When calculating the area on the basis of codes, the area enclosed by the outer boundary chain codes is positive, while the product of the area enclosed by the inner boundary chain codes is negative;
边元的取值同时也表明了相应的跟踪方向,某个边元方向为正,对水平边元而言,跟踪方向向右,对垂直边元而言,跟踪方向向上;The value of the edge element also indicates the corresponding tracking direction. A certain edge element direction is positive. For horizontal edge elements, the tracking direction is to the right, and for vertical edge elements, the tracking direction is upward;
跟踪过程中的标记打在当前边元的目标点上,以区分该边界点是否已经被跟踪过;The mark during the tracking process is placed on the target point of the current edge element to distinguish whether the boundary point has been tracked;
当当前考察点与参考点之间存在非零边元时,根据这两点所构成的2×2窗口中其余两点的情况,来决定下一步的跟踪方向,即后继边元,当返回起点时,跟踪过程结束。When there is a non-zero edge element between the current inspection point and the reference point, according to the situation of the remaining two points in the 2×2 window formed by these two points, the next tracking direction, that is, the subsequent edge element, is determined. When returning to the starting point , the tracking process ends.
其中,扫描过程采用网格扫描的方式寻找尚未跟踪过的边界点,链码采用8方向链码。上述边界跟踪核心算法极大地提高了算法效率。Among them, the scanning process adopts the method of grid scanning to find the boundary points that have not been tracked, and the chain code adopts 8-direction chain code. The above-mentioned boundary tracking core algorithm greatly improves the algorithm efficiency.
对图像做网格扫描,只有与网格线相交的区域才有可能被跟踪,那些完全落在网格内的区域则被忽略。因此,采用网格扫描也可以极大地提高处理速度。When doing a grid scan of the image, only areas that intersect the grid lines are likely to be tracked, and those that fall completely within the grid are ignored. Therefore, the use of grid scanning can also greatly improve the processing speed.
基于边界跟踪得到的链码串进行直线检测过程中,采用以下形式表示目标边界的链码串:In the process of straight line detection based on the chain code string obtained by boundary tracking, the following form is used to represent the chain code string of the target boundary:
I:Len,X,Y,d1,d2,…,dn I: Len,X,Y,d 1 ,d 2 ,…,d n
其中I为当前链码串的编号,Len为当前链码串中链码的总长度,X,Y为当前链码串起始点的图像坐标,d1,d2,…,dn为当前链码串上各像素的方向码,则直线检测的步骤为:Where I is the serial number of the current chain code string, Len is the total length of the chain code in the current chain code string, X, Y are the image coordinates of the starting point of the current chain code string, d 1 , d 2 ,..., d n are the current chain code string The direction code of each pixel on the code string, the steps of straight line detection are:
IV.最小直线段长度验证:顺序判断各条链码串,如果链码串的长度小于预设的最小直线段长度阈值LT,则舍弃该条链码串;IV. Minimum straight line segment length verification: sequentially judge each chain code string, if the length of the chain code string is less than the preset minimum straight line segment length threshold LT, discard the chain code string;
V.根据直线段近似度准则从链码串中提取直线段:V. Extract a straight line segment from a chaincode string according to the straight line segment approximation criterion:
顺序判断各条链码串;Sequentially judge each chaincode string;
①从起点开始,依次选择满足最小直线段长度约束的子链码串,根据式(1)计算该段的直线段近似度S,若S≥ST,ST为预设的最小直线段相似度阈值,则该段满足直线约束,转②,否则舍弃该段,继续判断下一段子链码串;①Starting from the starting point, select the sub-chain code strings that satisfy the minimum straight line segment length constraint in turn, and calculate the straight line segment approximation S of this segment according to formula (1). If S≥ST, ST is the preset minimum straight line segment similarity threshold , then the segment satisfies the linear constraint, go to ②, otherwise discard the segment and continue to judge the next sub-chain code string;
其中,||ps-pe||表示端点ps与pe之间的理想直线距离,Len(ps,pe)表示子链码串从端点ps到端点pe经过像素的实际长度,S则表示直线段近似度,另外,在计算链码串的实际长度时,若方向码为0、2、4、6,则链码连接的两像素间的实际长度为1,若方向码为1、3、5、7,则两像素间的实际长度为2(参见图2);Among them, ||p s -p e || represents the ideal straight-line distance between the endpoint p s and p e , and Len(p s , p e ) represents the actual pixel distance of the sub-chain code string from the endpoint p s to the endpoint p e length, and S represents the approximation of the straight line segment. In addition, when calculating the actual length of the chain code string, if the direction code is 0, 2, 4, or 6, the actual length between two pixels connected by the chain code is 1. If the direction The codes are 1, 3, 5, 7, then the actual length between two pixels is 2 (see Figure 2);
②若前一段子链码串也满足直线约束,则考虑相邻的两条子链码串能否合并成一条更长的直线段,将前一段链码串的起点到本段链码串的终点间的这段链码看作一条新的链码串,计算其直线段近似度S’。若S’≥ST,则进行合并,否则将本段子链码串标记成一条新的直线段;②If the previous sub-chain code string also satisfies the linear constraint, consider whether two adjacent sub-chain code strings can be merged into a longer straight line segment, and connect the starting point of the previous chain code string to the end point of this chain code string The chain code in between is regarded as a new chain code string, and its linear segment approximation S' is calculated. If S’≥ST, then merge, otherwise mark the sub-chain code string of this segment as a new straight line segment;
③若己到达链码串终点,则结束,否则转①继续判断下一段子链码串。③ If the end point of the chain code string has been reached, then end, otherwise go to ① and continue to judge the next sub-chain code string.
其中,所述预设的最小直线段长度阈值LT为23个像素,预设的最小直线段相似度阈值ST为0.91。Wherein, the preset minimum straight line segment length threshold LT is 23 pixels, and the preset minimum straight line segment similarity threshold ST is 0.91.
本发明是先对目标边界进行链码跟踪,然后在得到的链码串集合中进行直线段提取,其优点是计算量小,并且能同时得到直线段的位置、长度、方向等信息。本发明采用的直线检测算法从实际应用出发,摆脱了Freeman理想直线准则的约束,仅采用最小直线段长度和最小直线相似度两个约束参数,能够快速准确地实现直线检测。这种算法检测速度快、实用性强,增强了玉米茎秆识别过程的实时性。The present invention first traces the chain code on the boundary of the target, and then extracts the straight line segment from the obtained chain code string set. The straight line detection algorithm adopted in the present invention starts from practical application, gets rid of the constraint of Freeman's ideal straight line criterion, only adopts two constraint parameters of minimum straight line segment length and minimum straight line similarity, and can quickly and accurately realize straight line detection. This algorithm has fast detection speed and strong practicability, which enhances the real-time performance of the corn stalk identification process.
(6)计算所述二值化图像被检测得到的直线分割的不同区域的形状特征,并根据所述不同区域的形状特征对采集到的RGB彩色图像进行识别,得到最终的茎秆目标。(6) Calculate the shape features of the different areas of the binarized image segmented by the detected line, and identify the collected RGB color images according to the shape features of the different areas to obtain the final stalk target.
本步骤中,所述形状特征有多个参数,本实施例只计算形状参数和偏心率。将直线所在区域识别为茎秆,对被直线分割的其它区域利用形状参数和偏心率进行识别,满足F大于2.3且e大于5的区域识别为茎秆的步骤,其中,Area和Per分别为区域的面积和周长,c和a分别为区域的边界长轴长度和短轴长度,长轴是指区域边界上距离最远的两点连线,区域边界上与长轴垂直的连线中最长的线段称为短轴。In this step, the shape feature has multiple parameters, and in this embodiment, only shape parameters and eccentricity are calculated. Identify the area where the line is located as a stalk, and use the shape parameters for other areas divided by the line and eccentricity Identify the step of identifying the area that satisfies F greater than 2.3 and e greater than 5 as a stalk, where Area and Per are the area and perimeter of the area respectively, c and a are the length of the major axis and the length of the minor axis of the boundary of the area, respectively, The long axis refers to the line connecting the two points farthest on the boundary of the region, and the longest line segment among the lines perpendicular to the long axis on the boundary of the region is called the short axis.
以上实施方式仅用于说明本发明,而并非对本发明的限制,有关技术领域的普通技术人员,在不脱离本发明的精神和范围的情况下,还可以做出各种变化和变型,因此所有等同的技术方案也属于本发明的范畴,本发明的专利保护范围应由权利要求限定。The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Those of ordinary skill in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all Equivalent technical solutions also belong to the category of the present invention, and the scope of patent protection of the present invention should be defined by the claims.
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