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Tire X-ray Detection Method Based on BP Neural Network

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Published:24 February 2018Publication History

ABSTRACT

With the continuous improvement of the current social level and the continuous strengthening of automobile manufacturing technology, automobile production becomes more and more, people are convenient for travel, and the automobile has gradually become the mobility tool for most families. Cars gradually develop and at the same time also brought some bad vendors improper profit opportunities, many problemed radial tires are entered into the market. To ensure security, we need to detect radial tire before outing of factory, for X ray was widely used in tire testing technology, mature technology, electronic components and the detector is more perfect, so using X-ray scanning test, and the scanning image should have preprocessing and BP neural network recognition. Using MATLAB to study tire detection algorithm based on BP neural network. At the same time, designing the GUI interface for human interaction, facilitating user operations, to test requirements.

References

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  2. Yuan Peixin, Zhang Xiaohui. Application of digital image processing in tire X-ray detection {J}.CT theory and application research. In 2007, volume 16 (phase 2): 1--2Google ScholarGoogle Scholar
  3. Liu Honggui. Research on the detection algorithm of the defect detection of tire X-ray image {J}. Sensor world, 2014,(11):14--18.Google ScholarGoogle Scholar
  4. Jacqueline g.m. ertes,Norian Marranghello,Aledir s. Pereira.Real-time Module For Digital Image Processing, Developed on a FPGA{J}. IFAC Volumes, 2013 46--48 (28)Google ScholarGoogle Scholar
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  6. Li Lei. Design of digital image processing system based on MATLAB GUI {D}. Chengdu university of technology, 2012.Google ScholarGoogle Scholar

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  1. Tire X-ray Detection Method Based on BP Neural Network

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      cover image ACM Other conferences
      ICIGP '18: Proceedings of the 2018 International Conference on Image and Graphics Processing
      February 2018
      183 pages
      ISBN:9781450363679
      DOI:10.1145/3191442

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 February 2018

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