基于最优多尺度集的形态学红外图像缺陷检测研究

doi:10.19912/j.0254-0096.tynxb.2022-0051

太阳能学报 ›› 2022, Vol. 43 ›› Issue (6): 145-152.DOI: 10.19912/j.0254-0096.tynxb.2022-0051

基于最优多尺度集的形态学红外图像缺陷检测研究

康爽^1,2, 陈长征¹, 刘石³, 周勃¹, 唐琬茹²

1.沈阳工业大学机械工程学院,沈阳 110870;
2.白城师范学院机械与控制工程学院,白城 137000;
3.中国人民解放军63856部队,白城 137000

收稿日期:2022-01-12 出版日期:2022-06-28 发布日期:2022-12-28
通讯作者: 陈长征（1964—）,男,博士、教授、博士生导师,主要从事设备状态监测与故障诊断方面的研究。czchen@sut.edu.cn
基金资助:
国家自然科学基金（51675350; 52175105）; 吉林省教育厅科研项目（No.JJKH20220011KJ）

STUDY ON DEFECT DETECTION OF MORPHOLOGICAL INFRARED IMAGES BASED ON OPTIMAL MULTI-SCALE SET

Kang Shuang^1,2, Chen Changzheng¹, Liu Shi³, Zhou Bo¹, Tang Wanru²

1. Shool of Mechanical Engineering,Shenyang University of Technology, Shenyang 110870, China;
2. School of Mechanical and Control Engineering, Baicheng Normal University, Baicheng 137000, China;
3. Unit 63856 of the Chinese People’s Liberation Army, Baicheng 137000, China

Received:2022-01-12 Online:2022-06-28 Published:2022-12-28

摘要/Abstract

摘要： 图像增强在风力机叶片红外缺陷检测中具有重要应用,该文针对红外检测中图像清晰度低的问题,提出一种自适应迭代阈值加权的差分顶帽变换红外图像增强方法（AITWD top-hat）。首先,通过多尺度白顶帽与黑顶帽变换获得多组顶帽信息。其次,为获得更多的图像细节特征,采用对比度改善系数（contrast improvement ratio, CIR）作为阈值数,提出一种迭代阈值的图像加权方法,并对获得的最优尺度集图像进行融合,得到增强图像。最后,将该文所提方法与现有方法从定性和定量2个方面进行对比和验证。实验结果表明,该文方法不仅具有较好的图像细节增强效果,而且有效克服了传统形态学图像增强时稳定性差的问题,PSNR值和SSIM值分别提高了9.01%和9.07%。

关键词: 风力机叶片, 顶帽变换, 图像增强, 自适应阈值迭代

Abstract: Image enhancement is very important in infrared defect detection of wind turbine blades. Aiming at the problem of low image definition in infrared detection, an adaptive iterative threshold weighted difference top-hat transform infrared image enhancement method （AITWD top-hat） is proposed in this paper. Firstly, multiple groups of top-hat information are obtained by multi-scale white top-hat and black top-hat transformation. Secondly, in order to obtain more image detail features, using the Contrast improvement ratio（CIR） as the threshold function, an iterative threshold image weighting method is proposed, and the optimal scale set image is fused to obtain the enhanced image. Finally, the proposed method is compared and verified with the existing methods from both qualitative and quantitative aspects. The experimental results show that this method not only has good image detail enhancement effect, but also effectively overcomes the problem of poor stability in traditional morphological image enhancement, the PSNR value and SSIM value are increased by 9.01% and 9.07% respectively.

Key words: wind turbine blades, top-hat transform, image enhancement, adaptive threshold iteration

中图分类号:

TK83

康爽, 陈长征, 刘石, 周勃, 唐琬茹. 基于最优多尺度集的形态学红外图像缺陷检测研究[J]. 太阳能学报, 2022, 43(6): 145-152.

Kang Shuang, Chen Changzheng, Liu Shi, Zhou Bo, Tang Wanru. STUDY ON DEFECT DETECTION OF MORPHOLOGICAL INFRARED IMAGES BASED ON OPTIMAL MULTI-SCALE SET[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 145-152.

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基于最优多尺度集的形态学红外图像缺陷检测研究

STUDY ON DEFECT DETECTION OF MORPHOLOGICAL INFRARED IMAGES BASED ON OPTIMAL MULTI-SCALE SET

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