低对比度条件下联合收割机导航线提取方法

针对强光照下成熟小麦已收割区域和未收割区域对比度低、收割边界获取难度大的问题,该文提出了一种基于区域生长算法的联合收割机导航线精确提取方法。对摄像头采集的作物收割图像,利用区域生长算法分割出图像中未收割区域。生长阈值通过对图像灰度直方图高斯多峰拟合进行自适应计算。对分割得到的二值图像进行形态学处理,获取作物已收割和未收割区域分界线,然后采用最小二乘法拟合收割机作业导航线。试验结果表明:在小麦已收割和未收割区域对比度很低的情况下,所提方法能够精确地提取出小麦收获边线,并得到收割机作业导航线,与人工标定导航线夹角平均误差小于1.21°,可以为联合收割机的自动导航研究提供参考;处理一张900×1 200像素的图像时长约0.41 s,基本满足联合收割机导航作业的实时性要求。与传统方法对比发现,该文方法不易受作物生长密度和麦茬的干扰,导航线的提取精度更高,单幅图像的处理时间略有增加,但基本满足实时性作业要求。

Navigation line extraction method for combine harvester under low contrast conditions

Abstract: In grain harvesting , the combine harvester''s header need to guide along the harvest boundary in time to ensure the harvester is working in full widt, which requires the operator has high operational skills. In addition, with the long-time operation the driver is prone to fatigue, which brings safety hazards to agricultural production. Therefore, it is of great significance to study the automatic navigation technology of combine harvester to reduce the labor intensity of drivers and improve production efficiency. The key of automatic navigation is the extraction of navigation line. Due to richer environmental information, wider detection range and more complete information, visual-based navigation methods have attracted extensive attention. However, the contrast between cut and uncut areas of mature wheat in the image is extremely low under strong illumination, which leads that the harvest boundary of crop is quite blurred. To solve the problem that the cut edge is difficult to extract under low contrast conditions, a fast and accurate navigation line extraction method of combine harvester based on region growing algorithm is presented in this paper. Firstly, the color images of harvesting scene collected by camera was converted into the gray scale image, and the Gaussian filtering was applied to remove the image noise. Then, the region growing algorithm was used to segment the image. Initial seed was selected based on some criteria and then the uncut wheat area was segmented by region growing process. The gray value of each 4-neighboring pixel was compared with the mean gray value of the seed region, if their difference was smaller than the threshold the corresponding pixel was added to the seed region. This procedure was repeated until no pixel could be grouped in the region. To improve the robustness of the region growing algorithm, an adaptive threshold selection method based on gray histogram was proposed. The multi-peak Gaussian fitting of the gray histogram was performed and half of the absolute value of the difference between mean values of the two Gaussian components was taken as the threshold of region growing, then the segmented binary image was processed by the morphological operations to fill the small holes in the segmented region which made the harvest boundary of wheat smoother. Finally, the harvest boundary of crop was detected and the harvester navigation line was acquired by fitting the harvest boundary with the least squares method. The experimental results showed that even though the contrast of cut and uncut wheat areas was extremely low, the proposed method could accurately detect the wheat harvest boundary and extract the harvester navigation line. Under different operating conditions such as different light intensity and different crop growth density, the average angle error between the navigation line extracted by the proposed method and the manually calibrated navigation line was less than 1.21 °, processing a 900×1 200 pixels image took about 0.4 s, which basically meets the real-time requirements of the combine harvester navigation. The results can provide a reference for the automatic navigation of the combine harvester.