基于Census变换的双目视觉作物行识别方法

针对基于双目视觉技术的作物行识别算法在复杂农田环境下,立体匹配精度低、图像处理速度慢等问题,该文提出了一种基于Census变换的作物行识别算法。该方法运用改进的超绿-超红方法灰度化图像,以提取绿色作物行特征;采用最小核值相似算子检测作物行特征角点,以准确描述作物行轮廓信息;运用基于Census变换的立体匹配方法计算角点对应的最优视差,并根据平行双目视觉定位原理计算角点的空间坐标;根据作物行生长高度及种植规律,通过高程及宽度阈值提取有效的作物行特征点并检测作物行数量;运用主成分分析法拟合作物行中心线。采用无干扰、阴影、杂草及地头环境下的棉田视频对算法进行对比试验。试验结果表明,对于该文算法,在非地头环境下,作物行中心线的正确识别率不小于92.58%,平均偏差角度的绝对值不大于1.166°、偏差角度的标准差不大于2.628°;图像处理时间的平均值不大于0.293 s、标准差不大于0.025 s,能够满足田间导航作业的定位精度及实时性要求。

Method for detecting crop rows based on binocular vision with Census transformation

Machine vision is an important method for the navigation of agricultural machinery. The crop row detection is a key aspect of the navigation based on machine vision for pathway determination. The binocular vision based technique can locate the spatial position of crop rows, which is more effective for crop field with high weeds pressures than monocular vision based technique. The feature point detection and stereo matching are essential aspects of binocular vision algorithm, which will affect the accuracy and efficiency of crop row detection. To enhance the accuracy and efficiency of crop row detection for complex crop field, a new crop row detection method based on Census transform was presented in this paper. The presented method consisted of 3 modules that were image preprocessing, feature point detection and crop row detection. The image preprocessing module was composed of grayscale transformation and feature point detection. To separate crop rows from backgrounds, an improved function of excess green minus excess red (ExG? ExR) was used to transform RGB (red, green, blue) color image to grayscale image, in which only the ExG features were transformed by the classical ExG? ExR function. The improved ExG? ExR function was compared with the ExG and the classical ExG? ExR method. The comparative results showed that the improved ExG? ExR function was more effective to suppress the background noise. The smallest univalue segment assimilating nucleus (SUSAN) detector was used to detect corner points, which could describe the contour of crop rows. The module of feature point detection consisted of stereo matching and three-dimensional (3D) coordinate point calculation. An accurate stereo matching algorithm based on Census transformation was used to calculate the disparity of SUSAN corner point. The Census transformation of the SUSAN corner point was the primitive, and the sum of absolute difference (SAD) function was the similar metric. To reduce the computing burden and improve the accuracy, the sizes of Census mask and SAD mask were both 5×5 pixel. As the binocular camera (BB2-08S2C-38) used in this paper was assembled with 2 parallel monocular cameras, the 3D coordinates of SUSAN corner points were calculated based on their disparities. If the coordinates in the width and height axes of an SUSAN corner point were within the range of width and height thresholds, the point would be extracted to be a feature point of crop rows. As the crop rows were always parallel according to agronomic arrangement, the amount of crop rows in image could be estimated based on the frequency histogram of width distribution. The distance between adjacent crop rows was assigned to the interval, and the range of coordinates in width axis of feature points was assigned to the number of groups in the histogram. The feature points on each crop row were distributed with the shape of ellipse. After obtaining the amount of crop rows, feature points were used to locate the long axis of their distribution based on the principle component analysis (PCA) method. The long axis of the feature points was used to fit the centerline of the corresponding crop row. An SAD based crop row detection algorithm was set as a comparative algorithm. Videos of cotton field that were without infestations, with shadows, with weeds and in turnrows were used to test the 2 algorithms. Results showed that, the proposed algorithm was more robust to the changes of conditions and consumed a little more time; in the situations without turnrows, the accuracy of crop row detection was no less than 92.58%; for the deviation angle of detected centerlines of crop rows, the absolute mean value and standard deviation value were no more than 1.166 and 2.628° respectively; for the processing time, the mean value and standard deviation value were no more than 0.292 and 0.025 s. The accuracy and efficiency of presented method can satisfy the requirement of field operations.