基于核优化密度聚类的绿色苹果分割算法

苹果的可见光谱目标的高效、精准识别是实现果园测产或机器自动采摘作业的关键,由于绿色目标果实与枝叶背景颜色较为相近,因此绿色苹果的识别成为新的挑战.再由于果园实际复杂环境因素影响,如光照、阴雨、枝叶遮挡、目标重叠等情况,现有的目标果实识别方案难以满足测产或自动采摘的实时、精准作业需求.为更好地实现果园自然环境中绿色目标果...

KDC: A Green Apple Segmentation Method

Efficient and accurate recognition of apples in the visible spectrum is the key part of realizing orchard production prediction and automatic harvesting. The color of the green target is similar to the background of the branches and leaves.Thus the recognition of green apples becomes a new challenge. In the natural orchard, many complicated environmental factors may affect the accurate recognition of target fruit, such as light, rain, occlusion of branches and leaves, and overlap of targets. The existing recognition methods of target fruit are difficult to realize the real-time and accurate production prediction and automatic harvesting. In order to better realize the recognition of the green target in the natural orchard, a new kernel density clustering (KDC) is proposed. Firstly, the target image is segmented into irregular blocks by the simple linear iterative cluster (SLIC) algorithm, and approximate pixels in a small area are merged into superpixel area. The basic calculation unit is converted from a pixel point to a superpixel area to reduce the complexity. The SLIC algorithm is used to effectively avoid the target fruit contour’s blur when simplifying the image data. Based on superpixels, the two-dimensional feature components of the R-B region mean and the G-B region means are constructed, and the color feature space of the green apples for cluster analysis is established. Then, the density peak clustering algorithm calculates the local density and local difference degree of each data point of the green apple image. The preciseand accurate local density in different complicated scenes is calculated using kernel density estimation. The high-density area segmented by the low-density area is found more accurately, and the clustering of arbitrary shapes is realized. Finally, a decision graph used to find a cluster center is constructed according to local density and distance. A double sorting algorithm is employed to automatically select cluster centers and efficient segmentation of target fruit. The superpixel area representation of the image is obtained using the SLIC algorithm, and the local density of the data points is obtained using kernel density estimation. The new algorithm greatly reduces the computational complexity and achieves efficient and accurate segmentation of the target images. In order to better verify the performance of the new algorithm, the experiment collects occluded and overlapping target images under multiple lighting, rainy and other complex environments and evaluates them based on segmentation efficiency, segmentation effectiveness, false positives, and false negatives. By comparing the k-means clustering algorithm, mean shift clustering algorithm, fuzzy C-means (FCM) algorithm and density peak clustering algorithm (DPCA), it is finally concluded that the segmentation performance of the new algorithm is the best.