基于鲁棒Otsu的红外无损检测缺陷分割算法

汪子君; 邱俨睿; 杨宏霄; 孙磊

doi:doi:10.3788/irla201948.0204004

红外与激光工程, 2019, 48 (2): 0204004, 网络出版: 2019-04-05

基于鲁棒Otsu的红外无损检测缺陷分割算法

Algorithm for defect segmentation in infrared nondestructive testing based on robust Otsu

论文大纲

汪子君 ^*邱俨睿杨宏霄孙磊

作者单位

电子科技大学航空航天学院, 四川成都 611731

改善算法红外无损检测缺陷遗传算法 Otsu Otsu improved algorithm infrared nondestructive testing defects genetic algorithm

摘要

在红外无损检测获取的图像中, 缺陷区域与非缺陷区域所占面积比例悬殊, 且图像经过序列增强处理之后仍然存在阴暗区域, 导致缺陷分割准确性受损。为此, 结合局部阈值分割法的相对阈值思想, 提出一种基于鲁棒Otsu的缺陷分割算法。首先, 引入邻域均值与邻域总梯度作为表征像素点的所属类别与空间状态的重要参数。然后, 采用基于像素点-块区的统计调整模型对红外图像缺陷区和非缺陷区的灰度值进行动态调整。最后, 采用基于灰度-邻域偏差的改进二维直方图及其区域划分方法, 通过自动选取邻域边长的遗传算法搜索最佳阈值, 实现红外图像的缺陷分割。结果表明: 该算法不仅改善了Otsu算法的鲁棒性, 且能够提高红外无损检测缺陷分割的准确性。

Abstract

In infrared nondestructive testing, the proportion of defects is very different from that of background, and the low contrast region of infrared image has not been completely eliminated after image sequence enhancement, resulting in impaired accuracy of defect segmentation. In order to solve this problem, a defect segmentation method based on robust Otsu algorithm was proposed, which combined the relative threshold idea of local threshold segmentation method. Firstly, the mean value and the total gradient of the neighborhood were used to represent the category and spatial state of the pixels. Secondly, a point-block fusion statistical adjusted model on this basis was established for dynamically adjusting the gray scale values of the infrared image defects and non-defect regions. Finally, the improved two-dimensional histogram and its region division method based on gray value and neighborhood gray deviation was set for calculation of fitness function in genetic algorithm through which the optimal threshold could be determined from the mutative neighborhood size, then segmentation of defects could be achieved. The results show that this method improves the robustness of Otsu and the accuracy of defect segmentation.

参考文献

[1] 张金玉, 杨正伟, 田干. 红外热波检测及其图像序列处理技术[M]. 北京: 国防工业出版社, 2015.

Zhang Jinyu, Yang Zhengwei, Tian Gan. Infrared Thermal Wave Testing and Images Sequence Processing Technology[M]. Beijing: National Defence Industry Press, 2015. (in Chinese)

[2] 王冬冬, 张炜, 金国锋, 等. 尖点突变理论在红外热波检测图像分割中的应用[J]. 红外与激光工程, 2014, 43(3): 1009-1015.

Wang Dongdong, Zhang Wei, Jin Guofeng, et al. Application of cusp catastrophic theory in image segmentation of infrared thermal waving inspection[J]. Infrared and Laser Engineering, 2014, 43(3): 1009-1015. (in Chinese)

[3] Kumbhar P G, Holambe S N. A review of image thresholding techniques[J]. International Journal of Advanced Research in Computer Science and Software Engineering, 2015, 5(6): 160-163. (in Chinese)

[4] 王歆玥, 高旭辉. 一种自适应阈值分割方法[J]. 红外与激光工程, 2006, 35(S4): 167-171.

Wang Xinyue, Gao Xuhui. Image segmentation method of self-adopting threshold[J]. Infrared and Laser Engineering, 2006, 35(S4): 167-171. (in Chinese)

[5] 徐超, 黄风华, 毛政元. 一种改进的二维Otsu阈值分割算法[J]. 电子技术应用, 2016, 42(12): 108-111.

Xu Chao, Huang Fenghua, Mao Zhengyuan. An improved two-dimensional Otsu thresholding segmentation method[J]. Application and Electronic Technique, 2016, 42(12): 108-111. (in Chinese)

[6] 杨恢先, 颜微, 谭正华, 等. 改进的灰度-局部方差二维直方图图像分割[J]. 计算机工程与应用, 2017, 53(4): 209-213.

Yang Huixian, Yan Wei, Tan Zhenghua, et al. Improvement image segmentation based on average gray level-local variance two dimensional histogram[J]. Computer Engineering and Applications, 2017, 53(4): 209-213. (in Chinese)

[7] Jaafar H, Ibrahim S, Ramli D A. A robust and fast computation touchless palm print recognition system using LHEAT and the IFKNCN classifier[J]. Comput Intell Neurosci, 2015, 2015(5): 1-17.

[8] 符翔, 张剑, 王维, 等. 一种新的局部阈值分割算法[J]. 计算机应用与软件, 2015, 32(4): 195-197.

Fu Xiang, Zhang Jian, Wang Wei, et al. A new local threshold segmentation algorithm[J]. Computer Applications and Software, 2015, 32(4): 195-197. (in Chinese)

[9] Wan A M. A proposed optimum threshold level for document image binarization [J]. Advanced Research in Computing and Applications, 2017, 7(1): 8-14.

[10] 卓金武. MATLAB在数学建模中的应用[M]. 北京: 北京航空航天大学出版社, 2014.

Zhuo Jinwu. Application of MATLAB in Mathematical Model[M]. Beijing: Beihang University Press, 2014. (in Chinese)

汪子君, 邱俨睿, 杨宏霄, 孙磊. 基于鲁棒Otsu的红外无损检测缺陷分割算法[J]. 红外与激光工程, 2019, 48(2): 0204004. Wang Zijun, Qiu Yanrui, Yang Hongxiao, Sun Lei. Algorithm for defect segmentation in infrared nondestructive testing based on robust Otsu[J]. Infrared and Laser Engineering, 2019, 48(2): 0204004.

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