Picking point positioning method

A positioning method and picking point technology, applied in the field of computer vision, can solve the problems of low detection accuracy, difficulty in finding shear picking points, and low picking efficiency, and achieve a complete positioning process, high target detection accuracy, and a wide range of applications Effect

Pending Publication Date: 2019-09-10
SOUTH CHINA AGRI UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this type of device is that the detection accuracy is low, it i

Method used

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Examples

Experimental program
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Effect test

Embodiment 1

[0041] Such as figure 1 As shown, a positioning method based on picking points includes four aspects: 1. Field litchi image acquisition and data processing; 2. Model training of YOLO target detection algorithm; 3. DeepLab semantic segmentation model training; 4. OPTICS clustering and Related picking process.

[0042] 1. Image acquisition and data processing of wild litchi

[0043] The lychee images 1 in this experiment were taken by industrial cameras 3 respectively. The lychee varieties include Guiwei, Feizixiao, Huaizhi, and Nuomici. The weather conditions include rainy, cloudy and sunny days. The shooting time is from 8:00 to 17:00. The sampling data has a large difference, which is convenient to strengthen the robustness of the detection network and the difficulty of testing.

[0044] Because the experimental data is too large, it will bring unnecessary space and time costs to the network and data copy, so on the basis of the original data, the resolution of the picture ...

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Abstract

The invention discloses a picking point positioning method which comprises the following steps: obtaining a trained YOLOv3 target detection model, collecting and detecting a fruit growth area image, obtaining the number and positions of fruits in a detection view field, and fusing a feature extraction network of dense connection and residual idea with the target detection model; judging the numberof fruits in the visual field, and judging the images as a distant view and a close view according to the number; performing branch segmentation on the close-range image by using a semantic segmentation model to obtain a branch segmentation image; carrying out target detection on the distant view; judging whether the number of fruits in the branch segmentation image is 1 or not, if yes, using a single-fruit picking strategy, and if not, using a multi-fruit picking strategy; and acquiring final picking positioning point. According to the method, non-damage picking of mature bunches of fruits can be achieved, the shear point positions of the mature bunches of fruits are accurately found and positioned through an algorithm, fruit shear type picking is achieved, and integrity and picking efficiency of the fruits are guaranteed.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for locating litchi picking points in the wild based on convolutional neural network and density clustering. Background technique [0002] As a tool for harvesting vegetables and fruits in the future, picking robots can effectively solve the problems of labor shortage and huge labor costs. Through mechanized picking, they can achieve all-weather picking, picking under climate interference, and efficient multi-part picking at the same time. It is extremely important to increase efficiency, reduce labor costs, and accelerate agricultural development. The automated fruit and vegetable picking robot solves the time-consuming, laborious and costly problems in the fruit and vegetable picking process through mechanical automation. Among them, the selection and positioning of fruit and vegetable picking points, as an important part of the fruit and vegetable picking robot, occupi...

Claims

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Application Information

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IPC IPC(8): G06T7/73G06K9/62G06N3/04
CPCG06T7/75G06N3/045G06F18/23
Inventor 彭红星薛超钟景润李泽轩王炳锋
Owner SOUTH CHINA AGRI UNIV
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