基于凸壳理论的遮挡苹果目标识别与定位方法

为实现受果树枝叶遮挡、果实间相互遮挡的果实目标识别,该文提出了一种基于凸壳理论的遮挡苹果目标识别方法。该方法首先将图像由RGB颜色空间转换至L*a*b*颜色空间,并利用K-means聚类算法将图像分为树叶、枝条和果实3个类别,然后利用形态学方法对果实目标进行处理,得到目标边缘并进行轮廓跟踪,接着利用目标边缘的凸壳提取连续光滑的轮廓曲线,最后估计该光滑曲线段的圆心及半径参数,实现遮挡果实的定位。为了验证该算法的有效性,利用Hough圆拟合算法进行了对比试验,试验结果表明,该方法的平均定位误差为4.28%,低于Hough圆拟合方法的平均定位误差16.3%,该方法显著提高了目标定位的精度,能够有效识别遮挡苹果。

Recognition and localization methods of occluded apples based on convex hull theory

Aimed to realize the recognition and localization of occluded apples by branches, leaves and overlapped apples, a method for recognizing occluded apples based on convex hull theory was presented. In the first step, the image was transformed from RGB color space to L*a*b* color space, and K-means clustering algorithm was used to segment the input image to three different categories including leaves, stems and apples. In the second step, mathematical morphology was carried out to get the contours of apples. In the third step, the convex hull of object boundary was used to extract the smooth and real contours. In the last step, the centers and radiuses of the extracted contours were estimated, which were used to localize the occluded apples. In order to validate the performance of the algorithm presented in this study, a comparative test was conducted using Circle Hough Transform (CHT), and the positioning errors were calculated. The experimental results showed that the average positioning error of the method presented in this study was 4.28%, while that of the CHT method was 16.30%. The method significantly improves the accuracy of target positioning and is feasible and effective to recognize occlude apples.