基于K-means和近邻回归算法的Kinect植株深度图像修复

针对Kinect传感器应用于农业植株检测产生的图像噪声问题，特别是由光线以及传感器自身局限导致的匹配图像目标植株数据的缺失，提出一种基于K-means和近邻回归算法的植株深度检测图像修复方法。首先对Kinect传感器获取的彩色RGB图像进行阈值分割预处理提取植株目标区域，再利用K-means聚类算法去除背景噪声，使得植株目标区域轮廓更加清晰；然后基于配准的彩色图像和深度图像，对获取的深度图像中可疑像素点的深度数据采取近邻回归算法进行修复，再将修复后的深度图像与目标分割后的彩色图像进行植株区域的匹配，并进行二次近邻回归算法修正错误的深度数据，最后获取目标植株深度信息的检测图像。试验结果证明，采用RGB阈值分割和K-means聚类算法植株目标区域分割误差均值为12.33%，比单一RGB阈值分割和K-means聚类分割误差降低了12.12和41.48个百分点；同时结合聚类后的彩色图像对深度数据进行两次近邻回归算法修复深度数据，能够提高深度数据边缘的清晰度，单帧深度数据空洞点进行修复数据的准确度提高。该研究结果可为农业植株检测、植株三维重构、精准对靶喷雾等提供参考。

Kinect scanning plant depth image restoration based on K-means and K-nearest neighbor algorithms

Abstract: The Kinect sensor scanning images for agricultural plants are vulnerable to field light conditions and background noise, etc. In addition, the amount of data of the color image and depth image affect the efficiency and accuracy of the plant area, which leads to the difficulty of meeting the requirement for the Kinect sensor in agricultural plant detection. For the above problems, considering the influence of the light conditions and complex background information in agricultural environment on the quality of the plant detection and the depth data acquisition, in this paper, we proposed a plant depth detection image restoration method based on K-means and K-nearest neighbor. We also developed a novel method of image restoration to reduce the impact of background information to improve the accuracy of the color image segmentation, and to enhance the accuracy of depth data. Firstly, a RGB threshold segmentation algorithm was applied to original RGB-formatted plant color images to extract plant target areas from backgrounds. Three components R, G, and B were respectively separated from RGB color space, and the difference between G and R or B was primary extract of the plant area information. Meanwhile, for the color characteristic of the environment, a K-means clustering segmentation algorithm was performed on the extracted plant target areas to remove background noise and enhance target contours. Secondly, to fix the errors of the depth data and meet the requirements of the agricultural plant detection operations, the color image and depth image were registered to restore the suspicious pixels depth data based on K-nearest neighbor algorithm. Then, a K-Nearest Neighbor algorithm was presented to recovery the black hole pixels for depth images. Finally, we acquired the depth data of target plant from the detected images. Compared with conventional RGB threshold segmentation method and K-means algorithm method, the proposed method can be used to solve the problem of the color image noise. The experiment results showed that, the segmentation error can be reduced by 12.12% with RGB threshold segmentation method, and 41.48% with K-means algorithm method. The average segmentation error can be up to 12.33% by using RGB threshold segmentation first and then the K-means algorithm. Furthermore, the proposed method can be used to restore the depth data, and can significantly reduce the effect of the backgrounds. Thus it had a good improvement to the edge sharpness of the depth data, and the accuracy of the empty point depth data of single frame. The result of this study can be a reference for agricultural plant detection and 3D reconstruction, precision of target spraying.