Fruit and vegetable detection method based on deep learning

A deep learning and detection method technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems that restrict the research and application of fruit and vegetable picking robots, and the effect of fruit and vegetable picking robots is not ideal, so as to achieve simple and satisfactory testing. The effect of real-time requirements and reduced calculation

Inactive Publication Date: 2017-12-08
ZHEJIANG UNIV OF TECH
View PDF3 Cites 126 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because traditional machine vision methods have not yet been effectively resolved in terms of identifying fruits and vegetables from complex backgrounds and determining the spatial position of fruits and

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fruit and vegetable detection method based on deep learning
  • Fruit and vegetable detection method based on deep learning
  • Fruit and vegetable detection method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings, wherein:

[0041] The Faster R-CNN network is composed of a RPN convolutional neural network and a Fast R-CNN convolutional neural network, wherein the RPN convolutional neural network handles the regression problem, and in the present invention is mainly responsible for obtaining the candidates of various fruits and vegetables in the picture Region; Fast R-CNN convolutional neural network deals with classification problems. In the invention, it is responsible for further screening the candidate regions obtained by RPN, and distinguishing whether the candidate regions belong to the foreground or the background.

[0042] Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

[0043] Such as figure 1 As shown, w...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a fruit and vegetable detection method based on deep learning. The method comprises the following steps that: S1: firstly, preprocessing data, and carrying out manual calibration on an original picture in advance to obtain a segmentation tag, wherein the calibration means the coordinates of the left upper angular point and the right lower angular point of a target frame in the original picture, and the tag is used for judging whether a target in each calibration frame is a fruit and vegetable and determining the category of the fruit and vegetable; S2: secondly, training the data, taking the original picture and the picture tag as a training set of a deep learning neural network, and combining with a RPN (Region Proposal Network) and a Fast R-CNN to train the data to obtain a final fruit and vegetable detection model; and S3: finally, testing test data, calling a final fruit and vegetable detection model and a test program, carrying out fruit and vegetable detection on a test picture, and analyzing a final fruit and vegetable detection model effect through the observation of a test result.

Description

technical field [0001] The invention relates to a novel method for detecting fruits and vegetables, specifically through the Faster R-CNN network in deep learning to realize the detection of fruits and vegetables. Background technique [0002] Fruit and vegetable picking is the most time-consuming and labor-intensive link in the agricultural production chain, accounting for about 40% of the entire workload. The quality of picking operations directly affects the storage, processing and sales of fruits and vegetables, which ultimately affects market prices and economic benefits. Therefore, fruit and vegetable picking robots, as a form of agricultural robots, will become an inevitable product of the development of my country's agricultural modernization to a certain stage. As an important part of the picking robot, the vision system is the primary task and design difficulty of the fruit picking robot to identify the fruits and vegetables from the complex background and locate ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/2411
Inventor 胡海根周莉莉黄玉娇肖杰管秋陈胜勇
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap