[1]唐长明,钟剑锋,钟舜聪,等.基于改进自适应遗传算法和二维最大熵的 超声红外热像缺陷识别 [J].红外技术,2020,42(8):801-808.[doi:10.11846/j.issn.1001_8891.202008016]
 TANG Changming,ZHONG Jianfeng,ZHONG Shuncong,et al.Ultrasound Infrared Thermography Defect Recognition Based on Improved Adaptive Genetic Algorithm with Two-Dimensional Maximum Entropy[J].Infrared Technology,2020,42(8):801-808.[doi:10.11846/j.issn.1001_8891.202008016]
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基于改进自适应遗传算法和二维最大熵的
超声红外热像缺陷识别
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第8期
页码:
801-808
栏目:
出版日期:
2020-08-23

文章信息/Info

Title:
Ultrasound Infrared Thermography Defect Recognition Based on Improved Adaptive Genetic Algorithm with Two-Dimensional Maximum Entropy
文章编号:
1001-8891(2020)08-0801-08
作者:
唐长明1钟剑锋1钟舜聪12陈 曼1伏喜斌3黄学斌3
1. 福州大学 机械工程及自动化学院光学/太赫兹及无损检测实验室,福建 福州 350108;
2. 上海大学 机电工程及自动化学院,上海 200444;3. 厦门市特种设备检验检测院,福建 厦门 361000
Author(s):
TANG Changming1ZHONG Jianfeng1ZHONG Shuncong1 2CHEN Man1FU Xibin3HUANG Xuebin3
1. Laboratory of Optics, Terahertz and Non-destructive Testing, School of Mechanical Engineering and Automation, Fuzhou University Fuzhou 350108, China; 2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
3. Xiamen Special Equipment Inspection Institute, Xiamen 361000, China
关键词:
 声红外热像法自适应遗传算法二维最大熵
Keywords:
ultrasound infrared thermography adaptive genetic algorithm two-dimensional maximum entropy
分类号:
TG219
DOI:
10.11846/j.issn.1001_8891.202008016
文献标志码:
A
摘要:
根据超声红外热像检测图像的特点,为实现图像中缺陷的识别,提出了一种结合改进的自适应遗传算法和二维最大熵的分割方法,以实现准确、快速地分割出目标缺陷区域。该方法首先对超声红外图像进行预处理,得到了去噪后的图像,然后通过二维最大熵算法选取阈值将图像分为目标区域和背景区域,并结合改进的自适应遗传算法,提高分割速度。实验结果表明,该方法可以有效地滤除图像噪声,相比于穷举法和基于简单遗传算法的二维最大熵分割,本算法具有较好的分割速度和分割精度。
Abstract:
A segmentation method was developed for combining an improved adaptive genetic algorithm with a two-dimensional maximum entropy algorithm based on the image features of ultrasound infrared thermography detection for accurate and rapid segmentation of the target defect region to recognize defects of defect recognition in images. First, the infrared image was processed to obtain a denoised image. Next, the image was divided into the target and background regions using a two-dimensional maximum entropy algorithm; the segmentation speed was improved by combining it with the improved adaptive genetic algorithm. The experimental results showed that this method can effectively filter image noise. Compared with an exhaustive method and the two-dimensional maximum entropy segmentation based on a simple genetic algorithm, the proposed algorithm has better segmentation speed and accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-04-22;修订日期:2020-07-09.
作者简介:唐长明(1993-),男,福建安溪人,硕士研究生,主要从事超声红外无损检测及图像处理方面的研究。E-mail:565190908@qq.com。
通信作者:钟剑锋(1988-),男,福建龙岩人,讲师,硕士生导师。E-mail:zhongjianfeng@fzu.edu.cn。
基金项目:国家自然科学基金资助项目(51675103);机械系统与振动国家重点实验室开放课题基金资助(MSV-2018-07);上海市自然科学基金(18ZR1414200);福建省质量技术监督局科技项目(FJQI2016050);福建省科技计划对外合作项目。
更新日期/Last Update: 2020-08-20