Fruit flaw classification method and device based on machine vision and deep learning fusion, storage medium and computer equipment

Abstract

The invention provides a fruit flaw classification method and device based on machine vision and deep learning fusion, a storage medium and computer equipment. The method comprises the following steps: acquiring a color image of a fruit by using a camera, and respectively performing background segmentation algorithm processing, background region removal, HSI color transformation, Gaussian difference operation of an S space and the like on the acquired color image, thus obtaining a DoG image; then carrying out threshold segmentation on the DoG image, obtaining a defect region, positioning a target region in a color image, intercepting an image of the defect region, carrying out processing classification, endowing different label numbers, and constructing and training a differential convolutional neural network structure; and obtaining a network connection weight matrix, thereby completing defect classification of the to-be-detected image. According to the method, fruit classification is achieved; the advantages of machine vision and deep learning are fused, and the complexity of fruit flaw classification and recognition is fully considered, so that the recognition rate is improved, meanwhile, the recognition time is shortened, and the interference of fruit stems and calyx on classification and recognition due to angle and posture transformation is reduced.

Description

technical field [0001] The invention relates to the technical field of fruit classification in machine vision, in particular to a fruit defect classification method, device, storage medium and computer equipment based on the fusion of machine vision and deep learning. Background technique [0002] Due to the development of machine vision technology, in the process of fruit production and processing, computer vision can replace manual work for high-risk, high-intensity and high-repetition work. my country is a big fruit planting and consumption country. However, the current degree of automation of fruit grading technology in my country But it is relatively low, most of them are graded and classified by manual sorting, which is inefficient and requires a lot of manpower, and a small number of fruit classification based on computer vision is mainly used to distinguish defective fruits from normal fruits for rough classification. The classification of defective fruits, and then th...

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Problems solved by technology

[0002] Due to the development of machine vision technology, in the process of fruit production and processing, computer vision can replace manual work for high-risk, high-intensity and high-repetition work. my country is a big fruit planting and consumption country. However, the current degree of automation of fruit grading technology in my country But it is relatively low, most of them are graded and classified by manual sorting, which is inefficient and requires a lot of manpower, and a small number of fruit classification based on computer vision is mainly used to distinguish defective fruits from normal fruits for rough classification. The classification of defective fruits, and then the analysis of the main defects of fruits is relatively less involved. By classifying the defective fruits, the main defects of the fruits of this season can be found out, and the classification and grading can be carried out quickly and accurately during the fruit harvest season, and then reflect the quality of planting. Some deficiencies in the field, feedback analysis for fruit planting and production, and promote fruit production

[0003] The publication number is CN108491892A, which provides a fruit sorting system based on machine vision, which can intelligently distinguish the variety and color information of fruits. It is also the mainstream method at this stage. Grading, the recognition rate for defect classification is still low. After the introduction of deep learning, most of them use the whole image training, without considering the interference factors of the fruit stem and calyx, the algorithm complexity is high, and there will be problems such as long recognition time

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