Target fruit instance segmentation method and system

Abstract

The invention provides a target fruit instance segmentation method and system, which belong to the technical field of computer vision. The method comprises steps of for an acquired orchard environment image, processing the orchard environment image by using a pre-trained segmentation model to obtain an identification segmentation result, wherein the pre-trained segmentation model is obtained by training a training set, and the training set comprises a plurality of orchard environment images and labels for labeling target fruits in the images, and when the orchard environment image is processed by using a pre-trained segmentation model, conducting semantic category recognition and mask segmentation on the extracted features to obtain a target fruit instance segmentation result. According to the method, the dependence of a model on an anchor frame is avoided, the complexity of the model is reduced, and mask annotation is independently used for instance segmentation tasks in an end-to-end mode to optimize a network; the method does not depend on a model detection frame, directly obtains the pixel segmentation result of the instance, improves the target fruit recognition and segmentation speed, is high in robustness and real-time performance, and reduces the missing detection rate and the false detection rate.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method and system for segmenting target fruit instances based on an optimized SOLO-A network. Background technique [0002] For picking apples, tomatoes, strawberries and other fruits and vegetables or spraying medicines, using intelligent robots for machine picking or spraying medicines instead of traditional manual methods can greatly improve work efficiency. The rapid and accurate identification and positioning of the target directly affects the reliability and real-time performance of the picking robot for picking the target fruit. Accurate identification and positioning of target fruit is the key to the vision system. However, for complex orchard environments, there will be problems such as shading of branches and leaves, overlapping fruits, and rainy weather, which will make it more difficult for robots to identify fruits. [0003] In recent years, many excellen...

AI Technical Summary

Problems solved by technology

However, these methods are often not accurate enough to meet the requirements of real-time operations in complex orchard environments.

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