| 脉冲涡流热成像的自适应异常提取算法 |
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| 引用本文： | 王晓娜,杨沛,侯德鑫,叶树亮. 脉冲涡流热成像的自适应异常提取算法[J]. 仪器仪表学报, 2016, 37(8): 1818-1824 |
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| 作者姓名： | 王晓娜 杨沛 侯德鑫 叶树亮 |
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| 作者单位： | 中国计量大学工业与商贸计量技术研究所杭州310018,中国计量大学工业与商贸计量技术研究所杭州310018,中国计量大学工业与商贸计量技术研究所杭州310018,中国计量大学工业与商贸计量技术研究所杭州310018 |
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| 基金项目： | 浙江省自然科学青年基金（LQ16F030003）项目资助 |
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| 摘 要： | 脉冲涡流热成像通过检测试样表面温度场异常来进行缺陷检测。试样的表面不平整带来的非相关显示,使得缺陷引起的温度异常很难被检测出来。本文提出了自适应异常提取算法,该算法使用基于局部信息模糊C均值聚类图像分割将非相关显示的干扰区域分割开来,然后采用自适应模板对各个分割区域内的像素进行预测。该算法能够用于原始热图像和其他检测算法得到结果图的异常提取。以表面不平整的焊缝裂纹试样为对象来验证算法的有效性,并进行信噪比评价。结果表明该算法能够很好地抑制非相关显示,同时激励不均匀性的干扰也得到较好的消除,缺陷的异常信息被很好地提取出来。
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| 关 键 词： | 非相关显示；缺陷检测；图像分割；自适应异常提取；脉冲涡流热成像 |
Adaptive anomaly extraction algorithm for pulsed eddy current thermography |
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| Wang Xiaon,Yang Pei,Hou Dexin and Ye Shuliang. Adaptive anomaly extraction algorithm for pulsed eddy current thermography[J]. Chinese Journal of Scientific Instrument, 2016, 37(8): 1818-1824 |
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| Authors: | Wang Xiaon Yang Pei Hou Dexin Ye Shuliang |
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| Affiliation: | Institute of Industry and Trade Measurement Technique, China Jiliang University, Hangzhou 310018, China,Institute of Industry and Trade Measurement Technique, China Jiliang University, Hangzhou 310018, China,Institute of Industry and Trade Measurement Technique, China Jiliang University, Hangzhou 310018, China and Institute of Industry and Trade Measurement Technique, China Jiliang University, Hangzhou 310018, China |
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| Abstract: | Pulsed eddy current thermography detects defect through detecting anomalous temperature field on the specimen surface. Due to the non relevant indication caused by the uneven surface of the specimen, the temperature anomalies introduced by the defects are difficult to detect. In this paper, an adaptive anomaly extraction (AAE) algorithm is proposed based on image segmentation. The algorithm uses the image segmentation method based on the local information fuzzy c means clustering algorithm to segment the interference regions in the non relevant indication; and then the adaptive template is adopted to predict the pixels in the segmentation regions. This algorithm could be used to extract the temperature anomalies from original thermal images or the resultant images of other detection algorithms. In our study, a surface uneven welding seam crack specimen was taken as the object and experiment was conducted to verify the effectiveness of the AAE algorithm and carry out the signal to noise evaluation. The results show that the AAE algorithm can nicely reduce the influence of non relevant indication, eliminate the interference of stimulation non uniformity and extract the temperature anomalous information of the defect from thermal image. |
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| Keywords: | non relevant indication defect detection image segmentation adaptive anomaly extraction pulsed eddy current thermography |
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