基于核优化密度聚类的绿色苹果分割算法

doi:10.3964/j.issn.1000-0593(2021)09-2980-09

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Abstract 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.

Key words：Green fruit; Image segmentation; Kernel density estimation; Density peak clustering

Received: 2020-09-07 Accepted: 2021-01-05

ZTFLH:

TP317.4

Corresponding Authors: JIA Wei-kuan E-mail: wkjia@sdnu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)09-2980-09 OR https://www.gpxygpfx.com/EN/Y2021/V41/I09/2980