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| 融合K-means聚类分割算法与凸壳原理的遮挡苹果目标识别与定位方法 | |
| 作者姓名： | 江梅 孙飒爽 何东健 宋怀波 |
| 作者单位： | 西北农林科技大学机械与电子工程学院,陕西杨凌 712100 农业农村部农业物联网重点试验室,陕西杨凌 712100 陕西省农业信息感知与智能服务重点试验室,陕西杨凌 712100 |
| 摘 要： | 自然场景下苹果目标的精确识别与定位是智慧农业信息感知与获取领域的重要内容。为了解决自然场景下苹果目标识别与定位易受枝叶遮挡的问题,在K-means聚类分割算法的基础上,提出了基于凸壳原理的目标识别算法,并与基于去伪轮廓的目标识别算法和全轮廓拟合目标识别算法作了对比。基于凸壳原理的目标识别算法利用了苹果近似圆形的形状特性,结合K-means算法与最大类间方差算法将果实与背景分离,由凸壳原理得到果实目标的凸壳多边形,对凸壳多边形进行圆拟合,标定出果实位置。为验证算法有效性,对自然场景下的157幅苹果图像进行了测试,基于凸壳原理的目标识别算法、基于去伪轮廓的目标识别方法和全轮廓拟合目标识别方法的重叠率均值分别为83.7%、79.5%和70.3%,假阳性率均值分别为2.9%、1.7%和1.2%,假阴性率均值分别为16.3%、20.5%和29.7%。结果表明,与上面两种对比算法相比较,基于凸壳原理的目标识别算法识别效果更好且无识别错误的情况,该算法可为自然环境下的果实识别与分割问题提供借鉴与参考。 |
| 关 键 词： | 苹果识别 遮挡目标 凸壳原理 伪轮廓 K-means聚类算法 |
| 收稿时间： | 2019-03-10 |
Recognition and localization method of occluded apples based on K-means clustering segmentation algorithm and convex hull theory |
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| Authors: | Jiang Mei Sun Sashuang He Dongjian Song Huaibo |
| Institution: | College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China Ministry of Agriculture Key Laboratory for Agricultural Internet of Things, Yangling, 712100, China Key Laboratory of Agricultural Information Perception and Intelligent Services, Yangling 712100, China |
| Abstract: | Accurate segmentation and localization of apple objects in natural scenes is an important part of wisdom agriculture research for information perception and acquisition. In order to solve the problem that apples recognition and positioning are susceptible to occlusion of leaves in natural scenes, based on the K-means clustering segmentation algorithm, the object recognition algorithm based on convex hull theory was proposed. And the algorithm was compared with the object recognition algorithm based on removing false contours and the full-contour points fitting object recognition algorithm. The object recognition algorithm based on convex hull theory utilized that apples were like circle, combining K-means algorithm with Otsu algorithm to separate fruit from background. The convex polygon was obtained by convex hull theory and fit it circle to determine the position of the fruit. To verify the effectiveness of the algorithm, 157 apple images in natural scenes were tested. The average overlap rates of the object recognition algorithm based on convex hull theory, the object recognition algorithm based on removing false contour points and the full-contour points fitting object recognition algorithm were 83.7%, 79.5% and 70.3% respectively, the average false positive rates were 2.9%, 1.7% and 1.2% respectively, and the average false negative rates were 16.3%, 20.5% and 29.7% respectively. The experimental results showed that the object recognition algorithm based on convex hull theory had better localization performance and environmental adaptability compared to the other two algorithms and had no recognition error, which can provide reference for occluded fruits segmentation and localization in the natural scenes. |
| Keywords: | apple recognition occluded object convex hull theory false contour points K-means clustering algorithm |
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