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Green channel vehicle cargo carrying radioactive source image identification method based on convolutional neural network

A convolutional neural network and image recognition technology, applied in the field of image recognition, can solve the problems of difficult to become an inspection method, low accuracy, long time consumption, etc., and achieve the effect of solving memory explosion, reducing work intensity and reducing congestion.

Active Publication Date: 2019-10-29
CHANGAN UNIV
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Problems solved by technology

Manual evaluation is time-consuming, difficult and less accurate, so it is difficult to be an ideal inspection method

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  • Green channel vehicle cargo carrying radioactive source image identification method based on convolutional neural network
  • Green channel vehicle cargo carrying radioactive source image identification method based on convolutional neural network
  • Green channel vehicle cargo carrying radioactive source image identification method based on convolutional neural network

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Embodiment Construction

[0045] The present invention is further described below in conjunction with accompanying drawing:

[0046] see Figure 1 to Figure 4, the image recognition of radioactive sources of goods carried by green vehicles based on convolutional neural network mainly includes the following four parts, which are image preprocessing of radioactive sources of goods carried by green vehicles; image recognition model design of radioactive sources of goods carried by green vehicles; radiation of goods carried by green vehicles Source image recognition model tuning; verification of experimental results.

[0047] The details of each part are as follows:

[0048] 1. Preprocessing of images of radioactive sources carried by green vehicles

[0049] The image preprocessing of radioactive source images carried by green traffic vehicles mainly includes data cleaning, cargo area segmentation, noise reduction processing, data enhancement and channel conversion.

[0050] Image data cleaning of radio...

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Abstract

The invention discloses a green channel vehicle cargo carrying radioactive source image recognition method based on a convolutional neural network. The method comprises the following steps: step 1, preprocessing a green channel vehicle cargo carrying radioactive source image; 2, preparing an input image sample; step 3, designing a green channel vehicle cargo carrying radioactive source image recognition model; step 4, optimizing the green channel vehicle cargo carrying radioactive source image identification model; and step 5, by training, verifying and testing the model and recording changesof a loss function and classification accuracy in the training process, the loss function can reflect the capacity of the model for accurately classifying the cargo types. According to the invention,the green channel vehicle carried cargo radioactive source image identification based on the convolutional neural network is adopted, so that subjective dependence of an inspection result on inspection personnel can be avoided, and the working intensity of front-line inspection personnel is reduced. And meanwhile, the inspection efficiency can be improved, and the congestion condition of the tollstation is reduced.

Description

technical field [0001] The invention belongs to the technical field of image recognition, in particular to a convolutional neural network-based image recognition method for radioactive sources of goods carried by green traffic. Background technique [0002] Radiation source inspection usually uses gamma rays and x-rays. When the rays penetrate the object, the energy finally detected by the detector is the result of the rays being absorbed by various substances in this direction. The attenuation coefficient at this time is a function related to the spatial position and function of energy. In order to obtain the energy of different spatial positions, it is necessary to perform multi-directional projection on the inspected object to obtain the final energy value in each direction. After inverse Radon transformation, the energy value of each spatial position can be obtained. The energy value is mapped to the pixel value, and the image matrix is ​​obtained through reconstructio...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/462G06F18/241
Inventor 王萍张亚杰靳引利熊文磊闫龙昊王昊琛孙铸
Owner CHANGAN UNIV
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