基于自动Hough变换累加阈值的蔬菜作物行提取方法研究

为解决机器视觉对生菜和绿甘蓝两种作物在整个生长时期内多环境变量对作物行识别影响的问题,同时提高机器视觉作物行识别算法的有效性,该文提出了一种基于自动Hough变换累加阈值的多作物行提取算法。首先,选用Lab颜色空间中与光照无关a分量对绿色作物进行提取,通过最优自适应阈值进行图像分割,并采用先闭后开形态学运算对杂草和作物边缘进行滤波。其次,采用双阈值分段垂直投影法对作物行特征点进行提取,通过对亮度投影视图中的目标像素占比阈值和噪声判断阈值设置,实现特征点位置判断和杂草噪声过滤,并对相邻特征点进行优化,剔除部分干扰特征。最后,采用Hough变化对特征点进行直线拟合,将不同Hough变换累加阈值获得的拟合直线映射到累加平面上,通过K-means聚类将累加平面数据聚类为与作物行数相同的类数,根据相机成像的透视原理提出基于聚类质心距离差和组内方差的最优累加阈值获取方法,将最优累加阈值下累加平面中的聚类质心作为识别出的真实作物行线。温室和田间试验表明,针对不同生长时期的生菜和绿甘蓝作物,该文算法均可有效识别出作物行线,最优阈值算法耗时小于1.5 s,作物行提取平均耗时为0.2 s,在田间和温室中作物行的平均识别准确率分别为94.6%、97.1%,识别准确率为100%的占比分别为86.7%和93.3%。研究结果为解决多环境变量影响因素下的算法鲁棒性和适用性问题提供依据。

Vegetable crop row extraction method based on accumulation threshold of Hough Transformation

Abstract: Agricultural machinery field automatic navigation technology is widely used in farming, sowing, weeding, fertilizing, spraying, harvesting and other agricultural production process. This technology can improve the efficiency of the mechanical efficiency and reduce the missing areas of operation, labor intensity and the complexity of the operation. Because machine vision can be used to obtain and perceive the relative position information of crop rows, current crop growth status and field environment in real time, it is widely applied in online crop detection and identification. In this paper, a method based on automatic accumulation threshold of Hough Transformation was presented in order to improve the adaptability of the crop row recognition algorithm for different kinds and growth periods of vegetables with machine vision. The method was composed of image preprocessing, feature point detection, optimal accumulation threshold acquisition and crop row extraction. Firstly, to reduce the adverse effects of light change and restrain the background noise, a* component of Lab color model was selected for transforming RGB image to grayscale image. Optimal adaptive threshold and morphology close-open operation was applied for minimizing error segmentation probability and eliminating irrelevant detail. Secondly, the feature points of crop rows were extracted by sectionalized vertical projection method. The original image was divided into several horizontal segments and target pixel ratio and vertical projection width were used as double threshold in the luminance projection view of each segment to determine the location of feature points and distinguish noise. Thirdly, the Hough transformation method with different accumulative thresholds was performed to fit straight lines for all feature points in the image coordinate system, then they were all converted to Hough space accumulator as points. These points were clustered into the same number as crop rows by K-means clustering method. According to the camera projection, the optimal accumulator threshold was acquired by the position relation of clustering centroid and minimum inter-class variance. Finally, the fitting line parameters of real crop rows were the clustering centroid parameters of the accumulation space under the optimal accumulation threshold, then the parameters were converted into the crop lines in the image coordinate system. The crop row identification tests of lettuce and cabbage were carried out in the greenhouse and filed according to the conditions of crops in different growing periods, different weed densities, and different light conditions in the field. The greenhouse experiment showed that the algorithm can effectively identify crop rows with an average recognition accuracy of 97.1% for two crops of different growth periods under different weed densities. The outdoor experiment showed that the algorithm can also identify crop rows with 94.6% recognition accuracy under different row numbers and light conditions. Time consumption for optimal accumulator threshold algorithm and crop rows extraction algorithm were no more than 1.5 and 0.2 s, and the average accuracy rate of crop row detection was achieved 95.8%. In view of the practical application of field operations, as the environmental parameters basically do not change significantly in a short time, the optimal accumulation threshold was only needed to be obtained once, which can ensure the time consumption of algorithm was about 0.2 s.