A bar code area positioning method based on deep learning

Abstract

The invention provides a bar code area positioning method based on deep learning, which utilizes a deep learning technology to automatically conclude and extract the product positioning characteristics, can greatly improve the accuracy of characteristic extraction of product area positioning, and improves the recognition rate. And under the condition that the product is updated, an algorithm doesnot need to be developed additionally, so that the algorithm development period is greatly shortened, and the capability of being compatible with various products of the detection equipment is improved. The method comprises a training part and a prediction part, wherein the training part is used for collecting a large amount of training data and the label information in advance to form the training sets, at the training phase, firstly the data exploration and processing are performed, and the training module is used for establishing a convolutional neural network through the training set, thenpositioning the network to extract characteristics, then carrying out weight learning, judging whether to converge or not, generating a model file if the characteristics converge, returning to the positioning network to extract the characteristics if the characteristics do not converge, and carrying out model verification after the model file is generated.

Description

Technical field [0001] The present invention relates to the technical field of barcode area positioning, in particular to a barcode area positioning method based on deep learning. Background technique [0002] In the existing industrial bar code recognition industry, mainstream industrial bar code region positioning algorithms need to artificially define various rules for feature definition and modeling based on product characteristics. It mainly uses machine vision methods based on manual feature extraction and is collected by industrial cameras. Barcode images are passed into rule-based positioning methods, features are extracted, and detection results are output. Traditional detection methods have several problems: in the process of detection algorithm development, a lot of effort is needed to design and verify the algorithm prototype; after product upgrades, the algorithm needs to be re-developed, the generalization ability of the algorithm method is poor, and the development...

AI Technical Summary

Problems solved by technology

There are several problems in the traditional detection method: In the process of detection algorithm development, a lot of energy needs to be invested in the design and verification of the algorithm prototype; after the product is updated, the algorithm needs to be redeveloped, and the generalization ability of the algorithm method is poor; the development cycle is long; Positioning indicators are difficult to achieve the expected goals

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