缺株玉米行中心线提取算法研究

Extraction algorithm of the center line of maize row in case of plants lacking

无人驾驶农机自主进行行驶路径检测和识别系统需要具备环境感知能力。作物行的中心线识别是环境感知的一个重要方面,已有的作物行中心线识别算法在缺株作物行中心线提取中存在检测精度低的问题。该研究提出了一种能够在缺株情况下提取玉米作物行中心线的算法。首先采用限定HSV颜色空间中颜色分量范围的方法将作物与背景分割,通过形态学处理对图像进行去噪并填补作物行空洞;然后分别在图像底部和中部的横向位置设置条状感兴趣区(Region of Interest,ROI),提取ROI内的作物行轮廓重心作为定位点。在图像顶端间隔固定步长设置上端点,利用定位点和上端点组成的扫描线扫描图像,通过作物行区域最多的扫描线即为对应目标作物行的最优线;将获取的最优线与作物行区域进行融合填充作物行中的缺株部位;最后设置动态ROI,作物行区域内面积最大轮廓拟合的直线即为目标作物行中心线。试验结果表明,对于不同缺株情况下的玉米图像,该算法的平均准确率达到84.2%,每帧图像的平均检测时间为0.092 s。该研究算法可提高缺株情况下的作物行中心线识别率,具有鲁棒性强、准确度高的特点,可为无人驾驶农机在作物行缺株的农田环境下进行作业提供理论依据。

Identification of crop centerlines has been one of the most essential links in the environmental perception,particularly for the detection of driving paths during operation for the emerging unmanned agricultural machinery at present.However,the current detection of centerlines presents a low accuracy in the extraction of lacking rows for the maize seedling.In this study,an algorithm was proposed to extract the centerlines of maize rows in the lacking seedlings.The collection date was in July 2020,and the experimental subjects were maize seedlings.The height of the seedling was 0.3-0.4 m and the seedling spacing was 0.2-0.3 m at the time of image collection.The height of the camera was 1.5 m and the pitch angle was about 30°.The images of maize seedling rows were also collected in different plots of the experimental fields to ensure the universality of samples.Firstly,the range of HSV color components was limited to segment the seedlings and the background.The average time of threshold processing per frame of the image was 0.013 s.Morphological processing was utilized to fill the holes in the crop areas of denoised images.Secondly,a strip Region of Interest(ROI)was set in the horizontal position at the bottom and middle of the images.The barycenter was extracted from the seedlings contour located in the ROI as the locating points.Specifically,the upper endpoint was determined by the fixed step size in the pixel point of the first line of the image.The row area of the crop within a limited range was scanned using a straight line through the locating points and upper endpoint,where the line that crossed the most seedlings was the optimal line of target seedlings.As such,the contour feature of the seedling was strengthened,and the lack of seedling in the bottom area was filled,when the optimal line was fused with the seedling area.Because the algorithm was used to extract the crop centerline under different conditions of seedlings lacking,the optimal lines at the bottom and the middle of rows were fused with the region to fill the lacking part of the row.Finally,the dynamic ROI was set,where the fitting profile of the maximum area within the region was the target centerlines of seedling rows.The experimental results showed that the algorithm fully met the extracting requirement for the centerlines of seedlings in the field with seedling deficiency,compared with the traditional.It was also utilized to deal with the low detection rate when there was a seedling deficiency.Experimental verification was also performed on 1190 frames of maize seedlings images for the reliability of the algorithm in the lack of seedlings.The results showed that this algorithm required a relatively small amount of computation.Specifically,the average accuracy rate was 84.2%,and the average detection time of each frame was 0.092 s,indicating a better filling effect on the maize seedlings row with different crop lacked conditions.Consequently,the improved algorithm presented strong robustness and high accuracy for the recognition rate when seedlings were lacking.The finding can provide sound theoretical support to the operation of unmanned agricultural machinery in the field environment of seedlings lacking.