X-ray automatic inspection techniques for carbon product defects

ZHOU Xian; LIU Yi-lun

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X-ray automatic inspection techniques for carbon product defects

更新时间：2020-08-12

- X-ray automatic inspection techniques for carbon product defects
- Optics and Precision Engineering Vol. 14, Issue 3, Pages: 503-508(2006)
- 作者机构：
  
  中南大学机电工程学院,湖南长沙,410083
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Received：22 October 2005，
  
  Revised：14 May 2006，
  
  Published Online：30 June 2006，
  
  Published：30 June 2006
- 稿件说明：
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ZHOU Xian, LIU Yi-lun. X-ray automatic inspection techniques for carbon product defects[J]. Optics and precision engineering, 2006, 14(3): 503-508.
DOI：

ZHOU Xian, LIU Yi-lun. X-ray automatic inspection techniques for carbon product defects[J]. Optics and precision engineering, 2006, 14(3): 503-508. DOI：

摘要

针对炭素制品X光图像的特点

对其缺陷的提取与识别技术进行了研究

给出了目标边界提取算法和基于小波变换的图像增强算法

实现了图像的背景去除及增强处理。在此基础上

为排除噪声干扰的影响

采用数学形态学和迭代阈值分割相结合的方法从背景去除后的图像中提取出缺陷区域

取得了良好的效果。对缺陷特征选择及识别方法进行了研究

设计了基于遗传策略的特征选择和基于BP神经网络的缺陷识别算法

计算表明:缺陷正确识别率可达95%以上。采用上述技术开发完成了一套炭素制品缺陷X射线自动检测系统。

Abstract

Based on the characteristic of X-ray detection images of carbon product

the defect extraction and recognition techniques were present to remove background and enhance image successfully with target boundary extraction algorithm and image enhancement algorithm on wavelet transform. In order to eliminate the effect of noise

a mathematical morphology combining with iteration threshold segmentation method was adopted to extract defect from a image without background

it was proved to be very effective. An algorithm based on genetic strategy and BP neural network is proposed for the selection and recognition of defects

the results show that the veracity of defects recognition is 95%. These techniques are then combined into an automatic inspection system for carbon material.

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Keywords

references

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