Neural network-based ray image classification method

A neural network and classification method technology, applied in the field of radiographic image classification, can solve the problems of low radiographic image classification efficiency and classification accuracy, and achieve the effect of avoiding feature extraction and data reconstruction processes, reducing complexity and reducing the number of

Active Publication Date: 2017-09-08
BEIJING HANGXING MACHINERY MFG CO LTD
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Problems solved by technology

[0004] The technical solution of the present invention is to overcome the deficiencies in the prior art and provide a neural network-based ray image classification method. The present invention selects a neural network correspond...

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Embodiment Construction

[0035] The flow chart of the inventive method is as figure 1 As shown, before specifically describing the implementation process of the present invention, it should be noted that the use of the radial basis function of the neural network can reduce the noise interference of the X-ray image. Among them, the convolution operation of the radiation image by the convolutional layer of the convolutional neural network can enhance the characteristics of the radiation image and effectively suppress noise. The color characteristics of different materials in X-ray images and the fact that the size obtained when the spatial resolution is fixed does not change with the depth of field make the neural network more accurate and effective than ordinary grayscale images in the process of extracting complex features and data reconstruction. When the item security inspection model is set unchanged, the generated X-ray images have the same size, and the color stacking will occur when the items on...

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Abstract

The invention discloses a neural network-based ray image classification method and relates to the technical field of ray image classification. The method comprises the steps of (1) obtaining training samples of ray images and building a convolutional neural network model; (2) adjusting parameters of the convolutional neural network model in the step (1); (3) performing graphics preprocessing on the training samples in the step (1), inputting the preprocessed training sample to the adjusted convolutional neural network model in the step (2) for performing training, and obtaining feature information corresponding to the training samples; (4) extracting priori feature information corresponding to the training samples in the step (1); and (5) performing full connection on the priori feature information in the step (4) and feature information corresponding to a full connection layer of the convolutional neural network model in the step (3), and after class identifiers of the training samples corresponding to the priori feature information are stored, generating a ray image classification model.

Description

technical field [0001] The invention belongs to the technical field of radiographic image classification, and relates to a radiographic image classification method based on a neural network. Background technique [0002] With the wide application of multimedia technology and computer network, data storage and transmission become more convenient. A large amount of radiographic image data will be generated during security inspection, especially X-ray-based security inspection machines are widely used in stations, docks, airports, exhibition halls and other places. Among them, the X-ray fluoroscopy technology adopts the material identification method based on the dual-energy curve, which can quickly and accurately calculate the density or atomic number of the object to be fluoroscopy, and realize the qualitative and quantitative evaluation of the material on this basis. The obtained effective atomic number is used to render the X-ray image according to the material classificat...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V2201/05G06F18/2414
Inventor 何竞择徐圆飞张文杰
Owner BEIJING HANGXING MACHINERY MFG CO LTD
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