A method for segmenting adjacent siraitia grosvenorii based on depth image
To solve the problem that traditional RGB image processing algorithms can not segment adjacent Siraitia grosvenorii,a segmentation method of adjacent Siraitia grosvenorii based on depth image was proposed.By using the roundness shape characteristics and vine hanging characteristics of Siraitia grosvenorii,the depth image of the fruit was obtained from the bottom of the fruit.The theory of source point in fluid mechanics was introduced,and the gradient vector was regarded as the motion vector field,and the method of calculating the divergence of the vector field was proposed to realize the rough positioning of the fruit.K-means++clustering algorithm was used to segment the depth image,and a connected domain segmentation algorithm based on location points was proposed to segment a single fruit adjacent to Siraitia grosvenorii.The experimental results show that the algorithm has fast processing speed and high segmentation accuracy.A single image takes 0.139 second,with a segmentation accuracy of 94.43%,an accuracy of 94.20%,and a recall rate of 95.79%.Compared to the current algorithm,the accuracy is improved by 2.64%,and the processing speed is improved by 88.5%.Single and adjacent fruits can be segmented in complex lighting environments to avoid the limitations of color image processing.The research provides technical support for mechanized automatic picking of Siraitia grosvenorii.
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