Tire X-ray defect/flaw detection method based on deep convolutional neural network

A convolutional neural network and deep convolution technology, applied in the field of tire X-ray defect detection based on deep convolutional neural network, can solve the problems of strong contingency, inability to accurately detect tire defects, and low reliability

Active Publication Date: 2017-11-03
杭州盈格信息技术有限公司
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the shortcomings of the existing tire X-ray defect detection method, and provide a tire X-ray defect detection method based on a deep convolutional neural network to solve the existing tra

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  • Tire X-ray defect/flaw detection method based on deep convolutional neural network
  • Tire X-ray defect/flaw detection method based on deep convolutional neural network
  • Tire X-ray defect/flaw detection method based on deep convolutional neural network

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[0062] The implementation of the present application will be described in detail below with reference to the accompanying drawings and examples, so as to fully understand and implement the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects.

[0063] A tire X-ray defect detection method based on a deep convolutional neural network, the steps are as follows:

[0064] S1. Image preprocessing: see image 3 , to sharpen the original image. The original picture is not clear. After sharpening, the grain of the picture is clearer and the defect is more obvious.

[0065] S2. Data cleaning: cutting out the existing tire defect pictures, cutting out the defect part in the defect picture, as a negative sample; cutting out the normal part in the picture, as a positive sample.

[0066] S3. Design and training of convolutional neural network: see figure 1 , design four convolutional neural network models, tw...

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Abstract

The invention belongs to the technical field of image recognition and detection and specifically discloses a tire X-ray defect/flaw detection method based on the deep convolutional neural network. The detection method comprises the steps of image preprocessing, data cleaning, convolutional neural network designing and training, parameter adjusting, input image preprocessing, model training and model testing. According to the invention, tire x-ray images are classified based on the convolutional neural network. Compared with the traditional method, image features can be automatically learned during the training process of the convolutional neural network, so that the defect of manually selecting features can be avoided. The occurrence rate of accidental errors is reduced. The detection result of the method is high in reliability and tire defect/flaw can be accurately detected. The outstanding feature of applying the convolutional neural network to the image classification is that, a lot of time needs to be consumed during the training process of the model, while only a short period of time is required for the usage of the convolutional neural network model. Therefore, the cost is saved. The method is convenient for wide popularization.

Description

technical field [0001] The invention belongs to the technical field of image recognition and detection, and in particular relates to a tire X-ray defect detection method based on a deep convolutional neural network. Background technique [0002] Today's deep learning methods based on big data have far surpassed traditional recognition and detection methods. Convolutional Neural Network (CNN for short) is one of the more popular methods of deep learning at present, and it is a feedforward neural network. , its artificial neurons can respond to surrounding units within a part of the coverage area, and it has excellent performance for large image processing. Deep learning is a branch of machine learning that attempts to perform high-level abstraction of data using algorithms that contain complex structures or multiple processing layers composed of multiple nonlinear transformations. [0003] The convolutional neural network gradually extracts the high-level features of the ima...

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06T7/11G06T7/13
CPCG06T5/003G06T7/0004G06T2207/10116G06T2207/20081G06T2207/30108G06T7/11G06T7/13
Inventor 沈勤李莹杨颖范彬彬侯书文高建伟李全胜
Owner 杭州盈格信息技术有限公司
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