自然光照条件下采摘机器人果实识别的表面阴影去除方法  

Shadow removal method of fruits recognized by picking robot under natural environment

作　　者：熊俊涛[1] 卜榕彬 郭文韬 陈淑绵 杨振刚[1] Xiong Juntao;Bu Rongbin;Guo Wentao;Chen Shumian;Yang Zhengang(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642

出　　处：《农业工程学报》2018年第22期147-154,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31201135;31571568);广东省自然科学基金项目(2018A030313330);广州市科技计划项目(201802020032)

摘　　要：有效的阴影检测和去除算法会大大提高自然环境下果实识别算法的性能,为农业智能化提供技术支持。该研究采用超像素分割的方法,将一张图像分割成多个小区域,在对图像进行超像素分割的基础上,对自然光照下的果园图像阴影区域与非阴影区域进行对比分析,探索8个自定义特征用于阴影检测。然后采用SVM的方法,结合8个自主探索的自定义特征,对图像中每个超像素分割的小区域进行检测,判断每个小区域是否处于阴影中,再使用交叉验证方法进行参数优化。根据Finlayson的二维积分算法策略,对检测的每一个阴影区域进行阴影去除,获得去除阴影后的自然光照图像。最后进行阴影检测的识别准确性试验,试验结果表明,本研究的阴影检测算法的平均识别准确率为83.16%,经过阴影去除后,图像的阴影区域亮度得到了提高,并且整幅图像的亮度更为均匀。该研究可为自然环境下机器人识别果实及其他工农业应用场景提供技术支持。In the visual system of picking robot,the recognition and orientation of fruit is the key technology.In the natural environment,there is complex light and the shadows are ubiquitous physical phenomena in the natural world.The light would be blocked by branches,leaves,fruit,etc.,resulting in shadow casting on fruits.Shadow makes it more difficult for machine vision to identify and locate fruit,so it is significant to detect and remove shadows in the application of picking robot.In this paper,the shadow detection and removal method for fruit recognition by picking robot in the natural environment was studied to avoid the effect of light changes during the day,and the experiments were designed to verify the feasibility and effectiveness of the algorithm.In this study,we used bounding box manually to circumscribe and tag the branches,leaves,fruit,ground and sky of the citrus,litchi and longana images.The classification labels of the bounding box were set to shadow and no shadow firstly.The shadow region and the no shadow region of the orchard image under natural light were compared and analyzed.According to the characteristics of shadows,8 regional features,including average pixel value of regional grayscale,normalization of the feature,regional feature based on MSRCR transformation,regional feature based on MSRCR transformation,regional feature based on MSRCR transformation,reegional feature based on MSRCR transformation,regional feature based on MSRCR transformation and regional feature based on MSRCR transformation were studied and proved to be the effective features for shadow detection.The 8 self-explored custom features were extracted based on MSRCR and the classification labels of the above regions and trained by using the SVM.K-fold cross validation method was used to optimize the parameters of the SVM,and finally the optimal classification model was obtained.Secondly,the method of superpixel segmentation was used to divide an image into multiple small regions.Based on the superpixel segmentation of the image

关 键 词：机器人 图像处理 目标识别 阴影去除 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]