| 基于图像处理的果树滴水线导航路径检测方法研究 |
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| 引用本文： | 王恒,张维懿,李成松,王沛,王丽红.基于图像处理的果树滴水线导航路径检测方法研究[J].中国农机化学报,2023,44(3):183. |
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| 作者姓名： | 王恒 张维懿 李成松 王沛 王丽红 |
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| 作者单位： | 1. 西南大学工程技术学院，重庆市，400715； 2. 青岛理工大学信息与控制工程学院，山东青岛，266035；
3. 中国丘陵山区农业装备重点实验室，重庆市，400715；4. 西南大学柑橘研究所，重庆市，400715
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| 基金项目： | 重庆市技术创新与应用发展专项（cstc2021jscx—gksbX0007） |
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| 摘 要： | 针对果园开沟施肥，提出一种基于图像处理的果树滴水线导航路径检测方法。该方法采用垂直地面向上布置的CCD相机采集果树冠层投影图像，并实现果树冠层沿地面垂直投影轮廓的识别与滴水线平滑处理，进而对无人施肥装备沿果树环状行走路径进行确定。通过相机标定获取相机内部参数和畸变参数，对原始图像进行畸变矫正；通过对图像在RGB颜色空间的分布特征进行定量分析，使用平均值法对图像灰度处理，使用定阈值法进行二值分割；二值图像中由于存在大量的空间间隙，使用形态学膨胀操作，填充间隙，以凸显树冠投影边缘轮廓；使用边界跟踪算法，提取树冠轮廓边缘；引入Beseel曲线拟合方法，对轮廓边缘进行平滑处理，通过对比二阶、三阶、四阶、五阶拟合结果，得出使用三阶和四阶Beseel拟合结果较为符合导航路径要求。将相机固定在一个位置，分别在晴天和阴天拍摄条件下采集图像，进行滴水线导航路径提取，分别使用三阶和四阶Beseel曲线拟合晴天和阴天的图像边缘轮廓，使用四阶拟合结果较为符合实际要求，平均像素误差为19.5像素，平均像素相对误差为2.6%，平均每帧图像处理速度为27 ms，能较好地满足导航精度和实时性的要求，为施肥作业平台沿滴水线自动导航提供参考。
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| 关 键 词： | 果树滴水线 施肥 导航路径检测 图像处理 |
Research on the navigation path detection method of fruit tree drop line based on image processing |
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| Abstract: | In order to solve the problem that it is difficult to detect the visual navigation path of unmanned equipment walking around the fruit trees in the process of circular ditching and fertilization in the orchard, this paper proposes an image processing based navigation path detection method of fruit tree drip line. In this method, a CCD camera arranged vertically on the ground is used to collect the projection image of the fruit tree canopy, and the recognition of the vertical projection contour of the fruit tree canopy along the ground and the smooth processing of the drip line are realized, and then the circular walking path of the unfertilized equipment along the fruit tree is determined. In this paper, camera internal parameters and distortion parameters are obtained through camera calibration, and the original image is corrected. Through the quantitative analysis of the distribution characteristics of the image in RGB color space, the mean value method is used to process the image gray. Through the distribution characteristics of the original image and RGB, the environment and tree canopy projection are obviously different in gray value, and the threshold method is used for binary segmentation. Since there are a lot of noise and gaps in binary images, morphological expansion operation is used to fill gaps to highlight the edge contour of canopy projection. Boundary tracking algorithm is used to extract crown contour edges. Beseel curve interpolation fitting method is introduced to delete some inflection points and smooth the contour edges. By comparing the second order, third order, fourth order and fifth order fitting results, it is concluded that the third order and fourth order Beseel fitting results are more suitable for navigation path requirements. Fixed in one place, the camera shot in sunny and cloudy conditions respectively collectes image, extract drip line navigation path, respectively, the use of third order and fourth order Beseel curve fitting sunny and cloudy image edge contour, using the fourth order fitting results more in line with the actual requirements, the average error is 19.5 pixel, pixels on average relative error is 2.6%, The average image processing speed of each frame is 27 ms, which can better meet the requirements of navigation accuracy and real time. This study can provide reference for automatic navigation of fertilization platform along drip line. |
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| Keywords: | fruit tree drip line fertilization navigation path detection the image processing |
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