用动态图层融合与纹理-灰度梯度能量模型的果树行识别  

Fruit tree row recognition with dynamic layer fusion and texture-gray gradient energy model

作　　者：刘思瑶 王晓燕[1] 陈相安 侯秀宁 姜泰 谢鹏飞 张学敏[1] Liu Siyao;Wang Xiaoyan;Chen Xiang’an;Hou Xiuning;Jiang Tai;Xie Pengfei;Zhang Xuemin(College of Engineering,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学工学院,北京100083

出　　处：《农业工程学报》2022年第8期152-160,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：科技部创新方法工作专项(2016IM030200)。

摘　　要：为解决果园机器视觉导航中果树行识别易受果园复杂环境干扰的问题,该研究提出一种采用动态选取融合因子对彩色图像与深度图像进行图层融合并采用纹理-灰度梯度能量模型进行图像分割的果树行视觉识别算法。首先,通过搭建立体视觉系统获取果园彩色图像与对应的深度图像,并基于饱和度(S)通道图像的灰度值选取动态融合因子,实现对果园彩色图像与深度图像的图层融合;然后,分别计算融合图像的纹理特征图像与灰度梯度特征图像,并建立纹理-灰度梯度结合的能量模型,基于模型能量最小原则进行树干与背景的分割;最后,以树干与地面交点为果树行特征点进果树行直线拟合,完成果树行角度的识别。并对上述算法分别进行果树行识别试验与移动作业平台视觉对行导航试验。果树行识别试验结果表明,该研究算法果树行角度识别平均偏差为2.81°,与基于纹理、灰度梯度特征的果树行识别算法相比识别平均偏差分别降低2.37°和1.25°。移动作业平台视觉导航试验结果表明,在作业平台速度为0.6 m/s时,对行行驶最大偏差为12.2 cm,平均偏差为5.94 cm。该研究提出的视觉导航算法可以满足果园移动作业平台视觉对行导航需求,研究成果将为基于机器视觉的果园自动导航系统的研究与优化奠定基础。Row following operation has been one of the key technologies for smart management in an orchard. Machine vision can be widely expected to recognize the row in recent years. But the complex environment in the orchard can pose a great challenge to the image acquisition of the visual systems, such as the various lighting conditions and branches. There is also some interference of the visual system on the color image processing or depth information analysis of orchard images. In this study, a fruit trees row recognition was established to concurrently capture the texture features and depth information of the orchard image using machine vision. Two parts were included the color-depth images layers fusion and texture-gray gradient energy model, in order to promote the based image segmentation that combined with the color image and depth image. Firstly,a binocular stereo vision system was built to obtain the orchard color image and depth image at the same time with the stereo vision calibration, according to orchard planting features and row recognition requirements. The gray value of saturation(S)channel image of HSV color space was used as the dynamic fusion factor to realize the layer fusion of orchard color and depth image, in order to avoid the loss of texture features, color or depth information that caused by layer fusion with the static fusion factor. Secondly, the texture and gray gradient features were calculated in the fused orchard image. A comparative analysis found that there was less interference in the trunk regions. More importantly, there was an outstanding gray gradient without the texture feature in orchard trunk regions. An energy model was then established to combine the texture and gray gradient features. The trunk region and background in the channel-fused orchard image were segmented easily using the minimum energy of the model. As such, the region segmentation of the trunk was completed with the layer fusion and energy model. Specifically, the intersection points of trunks and ground were taken

关 键 词：机器视觉 图像处理 导航 深度图像 图层融合 果树行识别 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]