红外与激光工程, 2018, 47 (11): 1104005, 网络出版: 2019-01-10   

数字细节增强技术在脉冲热成像无损检测中的应用

Application of digital detail enhancement technology in pulsed thermography NDT
作者单位
北京理工大学 光电学院 光电成像技术与系统教育部重点实验室, 北京 100081
摘要
针对脉冲热成像法红外检测图像对比度低、缺陷目标边缘模糊、受不均匀照明影响大的问题, 提出一种基于目标轮廓的结合数字细节增强技术与多阈值最大熵的缺陷大小定量估算方法。首先, 脉冲红外检测图像经自动对比度增强算法优化的数字细节增强方法处理后, 缺陷与背景间对比度显著提升, 从而不均匀照明对缺陷识别效果的影响明显减弱; 其次, 再通过遗传算法优化的最大熵多阈值分割方法提取缺陷目标, 对其进行八邻域法轮廓跟踪, 以提取各缺陷区域的轮廓像素点并排序; 最后, 对具有一定方向的缺陷轮廓分别采用欧氏距离法和格林公式对缺陷的周长和面积进行定量估算。实验结果表明: 该方法对缺陷大小进行定量估算的可行性, 且数字细节增强技术可在一定程度上提高脉冲热成像检测系统的缺陷探测水平。
Abstract
Pulsed thermographic image has the disadvantages of low-contrast, fuzzy-edge, and non-uniformity of illumination for the defect detection, thus, a defect determination method which combines digital detail enhancement (DDE) technology with maximum entropy multi-threshold segmentation method was proposed for the improvement of pulsed thermographic image. Firstly, the contrast between defects and the background was improved significantly after the image was processed with digital detail enhancement algorithm optimized with adaptive contrast enhancement (ACE) algorithm, and reducing the influence of illuminative non-uniformity on defect recognition. Secondly, the target defects with maximum entropy multi-threshold segmentation method optimized with genetic algorithm, and the contours of each defect with eight neighborhood method to get the contour pixels in a certain sequence. Finally, based on the sequential contour pixels, the perimeter and the area of each defect could be estimated respectively with Euclidean distances formula and Green's theorem. The experimental result shows that this method is feasible to estimate defect size quantitatively, and digital detail enhancement technology could improve the defect detectability of pulsed thermographic system in a certain extent.

徐超, 陈一鹤. 数字细节增强技术在脉冲热成像无损检测中的应用[J]. 红外与激光工程, 2018, 47(11): 1104005. Xu Chao, Chen Yihe. Application of digital detail enhancement technology in pulsed thermography NDT[J]. Infrared and Laser Engineering, 2018, 47(11): 1104005.

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