Fruit recognition method and device based on improved SOLO network and fruit picking robot

Abstract

The invention provides a fruit recognition method and device based on an improved SOLO network, and a fruit picking robot, and belongs to the technical field of fruit picking robots. The method comprises the steps: obtaining a fruit tree picture in an orchard environment; inputting the fruit tree picture into a recognition model based on an improved SOLO network, and determining whether a fruit exists in the fruit tree picture or not; wherein the recognition model based on the improved SOLO network is obtained by using multiple groups of data through machine learning training; wherein each group of data in the plurality of groups of data comprises a picture with fruits and label information for marking the picture with the fruits. According to the fruit recognition method based on the improved SOLO network, the problem that the fruit segmentation efficiency of a visual system of a picking robot is poor is solved, the method is simple, the practice speed is high, a relatively accurate segmentation result can be obtained, and the fruit recognition method is suitable for real-time operation of the picking robot.

Description

technical field [0001] The invention relates to the technical field of fruit picking robots, in particular to a fruit identification method and device based on an improved SOLO network and a fruit picking robot. Background technique [0002] my country's fruit production ranks first in the world, but manual picking is still the main method. With the intensification of population urbanization and aging trend, the labor cost in social production is increasing day by day. The development of a fruit picking robot instead of manual picking can not only improve labor efficiency and economic benefits, but also promote the intelligent development of agricultural machinery. has strong practical significance. [0003] The rapid and accurate segmentation of target fruits directly affects the reliability and real-time performance of picking robots. The precise segmentation of objects is the key to the vision system. The picking of green fruit is a part that cannot be ignored, and it ...

AI Technical Summary

Problems solved by technology

Although some progress has been made, there are still some problems in these methods: a large amount of computing and storage resources are required, and power consumption and stability issues need to be weighed; deep convolutional neural network models are generally large, the segmentation speed is not fast, and it is difficult to migrate and deploy to embedded mobile devices for real-time segmentation of green fruits under natural conditions

Images