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Tire X-ray image detection and identification method and system

A recognition method and optical image technology, applied in the field of image recognition, can solve the problems of heavy workload and poor versatility of workers, and achieve the effects of unified judgment standards, high accuracy and improved judgment efficiency

Active Publication Date: 2016-02-17
MESNAC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a tire X-ray image detection and recognition method and system in order to solve the technical problems of heavy worker workload and poor versatility in the existing tire X-ray image detection and recognition method

Method used

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  • Tire X-ray image detection and identification method and system
  • Tire X-ray image detection and identification method and system

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Experimental program
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Embodiment 1

[0048] Embodiment one, see figure 1 , figure 2 As shown, the present embodiment proposes a tire X-ray image detection and recognition method, including an offline training step and an online detection step, and the offline training step includes the following sub-steps:

[0049] S11. Establish a training sample, including a number of tire X-ray images and corresponding judgment results;

[0050] S12. Perform defect detection on images in the training samples, and calculate defect information of the training samples;

[0051] S13. Quantify and encode the training sample defect information into a training sample feature vector to form an n-dimensional vector, and the parameters n represent n types of defects respectively;

[0052] S14. Using the classification result of the image in the training sample as a sample label, train the feature vector of the training sample into a classification model;

[0053] The online detection step includes the following sub-steps:

[0054] ...

Embodiment 2

[0060] Embodiment 2, in step S13, the judgment model in this embodiment preferably adopts an incremental extreme random forest classifier. Random forest is a classifier that contains multiple decision trees, in which the construction and classification test of each decision tree are independent of each other, and the classifier output is the mode of all decision tree outputs.

[0061] In the offline training step, the traditional method is used to detect defects in the tire X-ray images in the training samples, and calculate defect information, such as the size of impurities, the number of broken ends of steel cords, etc. The defect information is quantized and encoded into a feature vector, and different types of defect information are stored in different dimensions. Using the result of manual classification as the true value of the category, the tire X-ray image training samples are used to train a linear or nonlinear classification model to characterize the mapping relation...

Embodiment 3

[0077] Embodiment 3, based on the tire X-ray image detection and recognition methods in Embodiment 1 and Embodiment 2, this embodiment provides a tire X-ray image detection and recognition system, including an offline training subsystem and an online detection subsystem. The offline training subsystem includes at least a database and a training unit storing training samples, and the training unit is used for:

[0078] Perform defect detection on the images in the training samples, and calculate the defect information of the training samples;

[0079] The training sample defect information is quantized and encoded as a training sample feature vector to form an n-dimensional vector, said

[0080] The parameter n represents n types of defects respectively;

[0081] Using the classification result of the image in the training sample as a sample label, training the training sample feature vector into a classification model;

[0082] The online detection subsystem includes:

[00...

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Abstract

The invention discloses a tire X-ray image detection and identification method and system. The detection and identification method comprises an offline training step and an online detection step. The offline training step comprises: (11), establishing a training sample; (12), performing defect detection on an image in the training sample, and computing defect information of the training sample; (13), performing quantization coding on the defect information of the training sample to obtain a training sample feature vector; and (14), training the training sample feature vector and a grade judging result into a grade judging model. The online detection step comprises: (21), collecting a to-be-detected tire X-ray image in real time; (22), performing detect detection on the to-be-detected tire X-ray image, and computing all to-be-detected tire defect information; (23), performing quantization coding on all the to-be-detected tire defect information; and (24), computing the to-be-detected tire feature vector and the grade judging model. According to the detection and identification method, artificial subjective factors are avoided, the judgment is more objective and unified, and the judgment efficiency and accuracy are both improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition, in particular to a tire X-ray image detection and recognition method and system. Background technique [0002] In the rubber tire industry, the detection of internal structural defects of tires is mainly carried out by X-ray machines. The process is to first scan and image the tires with X-ray transmission, and then realize the detection by interpreting the X-ray images. The current X-ray image interpretation work is mainly done manually, its high work intensity, low efficiency and high misjudgment rate make it no longer meet the requirements of tire modernization quality inspection. [0003] On this basis, there are also automatic defect detection and grading methods using machine vision, but the grading system of this method is based on the measurement parameters of various defects, and tire quality experts formulate unified grading rules. Compare the measurement results with the rul...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/66G01N23/04
Inventor 张斌高书征
Owner MESNAC
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