玉米行间导航线实时提取

针对高地隙植保机底盘玉米田间植保作业压苗严重的现象,该研究提出了基于车轮正前方可行走动态感兴趣区域(Region of Interest,ROI)的玉米行导航线实时提取算法。首先将获取的玉米苗带图像进行像素归一化,采用过绿算法和最大类间方差法分割玉米与背景,并通过形态学处理对图像进行增强和去噪;然后对视频第1帧图像应用垂直投影法确定静态ROI区域,并在静态ROI区域内利用特征点聚类算法拟合作物行识别线,基于已识别的玉米行识别线更新和优化动态ROI区域,实现动态ROI区域的动态迁移;最后在动态ROI区域内采用最小二乘法获取高地隙植保机底盘玉米行间导航线。试验表明,该算法具有较好的抗干扰性能,能够很好地适应较为复杂的田间环境,导航线提取准确率为96%,处理一帧分辨率为1 920像素×1 080像素图像平均耗时97.56 ms,该研究提出的算法能够为高地隙植保机车轮沿玉米垄间行走提供可靠、实时的导航路径。

Real-time extraction of navigation line between corn rows

High-clearance plant protection machine plays an important role in the large-scale chemical fertilizer spraying machinery in China. The wheels of high-clearance plant protection machine must be driven along the ridge during the working process. However, the driver''s vision is blocked by corn plants, which is easy to cause corn seedlings damaged by tires. In view of the serious phenomenon of seedling injury during the operation of high-clearance plant protection machine, a method of real-time extraction of navigation line between the corn rows based on a dynamic region of interest (ROI) was proposed in this paper. According to the different growing periods of corn, the videos were collected when the average height of corn plant was 30, 50 and 70 cm under different light conditions. Through reducing the influence of light by pixel normalization, segmenting the image of corn and background by green image processing algorithm and the maximum class square error method, the images were enhanced and removed by morphological processing. Firstly, the collected image was divided into 10 image bands, and each image band was processed by vertical projection method to locate the center lines between crop rows in the first frame of the video. The approximate position of the static ROI was determined and marked based on the perspective principle of camera and geometric information of the corn seedling belts. It took 473.7 ms to divide the static ROI, which does not affect the real-time performance of navigation since the determined of static ROI was prior to the navigation operation. Secondly, the static ROI was applied in the second frame of video, in which the navigation line between the corn rows was obtained by sub-regional feature points clustering algorithm. According to the geometric information of navigation line and ROI, the ROI was updated and optimized. Then the updated ROI was applied to the navigation line extraction of the next video frame. 225 consecutive videos were selected to divide the ROI manually, and the dynamic ROI obtained by proposed method was compared with the artificial ROI. The pixel deviation between the artificial ROI and the ROI was about 10 pixels, which shown that the algorithm had good convergence and met the requirements of navigation accuracy. Finally, a comparative experiment of artificial navigation line extraction and the algorithm in this paper by randomly selecting 100 frames of image. The results showed that the algorithm proposed by this paper could automatically obtain the dynamic ROI, the average error of navigation line extraction between manual and algorithm in this paper was 1.157°, and the accuracy rate of corn seedling belt recognitionwas 96%, and the average processing time of a real-time image with a resolution of 1 920 pixels×1 080 pixels was 97.56 ms. July 16, 2019, the navigation experiment was carried out in corn field, and the heading angle was used as the input signal of the automatic steering control system. The results showed that the wheels of high-clearance plant protection machine walked along the corn rows, and the wheel angle floated up and down at 0°. The algorithm proposed in this paper can meet the accuracy and real-time requirements of the automatic walking for high-clearance plant protection machine in corn.