基于自适应均值的地铁隧道裂缝图像滤波算法

doi:10.3969/j.issn.1674-0696.2019.06.01

重庆交通大学学报（自然科学版） ›› 2019, Vol. 38 ›› Issue (06): 1-5.DOI: 10.3969/j.issn.1674-0696.2019.06.01

• 交通+大数据人工智能 • 下一篇

基于自适应均值的地铁隧道裂缝图像滤波算法

张振海1,2，尹晓珍1,2，任倩1,2

(1. 兰州交通大学自动化与电气工程学院，甘肃兰州 730070； 2. 甘肃省人工智能与图形图像处理工程研究中心，甘肃兰州 730070) 2. 北方工业大学城市道路交通智能控制技术北京市重点实验室，北京 100144)

收稿日期:2018-04-18 修回日期:2018-06-14 出版日期:2019-06-14 发布日期:2019-06-14
作者简介:张振海(1983—)，男，河南林州人，博士，副教授，主要从事交通信息控制和数字图像处理的教学与科研工作。E-mail:764411629@qq.com。通信作者：尹晓珍(1991—)，女，山西朔州人，硕士，主要研究方向为数字图像处理。E-mail:374213791@qq.com
基金资助:
国家自然科学基金项目(61763025)；甘肃省自然科学基金项目(18JR3RA124)；中国博士后科学基金项目(167306)；兰州市人才创新创业项目(2015-RC-8)；教育部创新团队发展计划项目(IRT_16R36)

Image Filtering Algorithm for Subway Tunnel Cracks Based on Self-adaptive Mean Hardware-in-the-loop Simulation

ZHANG Zhenhai1,2，YIN Xiaozhen1,2， REN Qian1,2

(1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, P. R. China; 2. Gansu Artificial Intelligence and Graphic Image Processing Engineering Research Center, Lanzhou 730070, Gansu, P. R. China) P. R. China; 2. Beijing Key Lab of Urban Road Traffic Intelligent Control Technology, North China University of Technology, Beijing 100144, P. R. China)

Received:2018-04-18 Revised:2018-06-14 Online:2019-06-14 Published:2019-06-14

摘要/Abstract

摘要： 地铁隧道表面光照不均匀、对比度低、噪声干扰严重，采集到的裂缝图片较灰暗且含有大量混合噪声，因而单一的滤波方法不能达到很好的去噪效果。针对该问题，提出一种基于快速中值的自适应均值滤波算法来实现隧道裂缝图像滤波。该算法先对图像进行反转以增强裂缝与背景的对比度，通过快速中值法计算滤波窗口的中值，自适应地选取合适的阈值来对滤波系数加以优化，并将窗口各像素点的灰度值进行加权运算，其结果作为窗口中心点灰度值，并输出。通过与传统中值、均值滤波算法比较，提出的新算法不仅降低了图像噪声，而且有效地保护了裂缝边缘，降低后续对裂缝特征量提取及分割识别的难度。

关键词: 隧道工程, 地铁隧道, 裂缝, 噪声, 滤波, 自适应

Abstract: The surface of subway tunnel is of uneven illumination, low contrast and serious noise interference. The collected crack images are rather dark and contain a lot of mixed noise. So the single filtering method can not achieve good denoising effect. In order to solve this problem, a self-adaptive mean filtering algorithm based on fast median was proposed to filter the image of tunnel cracks. The proposed algorithm first reversed the image to enhance the contrast between the crack and the background, and then calculated the median of the filtering window by fast median method. The filter coefficients were optimized by selecting appropriate thresholds adaptively, and the gray values of each pixel in the window were weighted. The results were taken as the gray values of the center of the window and outputted. Compared with the traditional median and mean filtering algorithms, the proposed new algorithm not only reduces the image noise but also effectively protects the edge of the fracture, and reduces the difficulty of follow-up crack feature extraction and segmentation recognition

Key words: tunnel engineering, subway tunnel, cracks, noise, filtering, self-adaption platform, evaluation on control effectiveness

中图分类号:

U457
TP391

张振海1,2，尹晓珍1,2，任倩1,2. 基于自适应均值的地铁隧道裂缝图像滤波算法[J]. 重庆交通大学学报（自然科学版）, 2019, 38(06): 1-5.

ZHANG Zhenhai1,2，YIN Xiaozhen1,2， REN Qian1,2. Image Filtering Algorithm for Subway Tunnel Cracks Based on Self-adaptive Mean Hardware-in-the-loop Simulation[J]. Journal of Chongqing Jiaotong University(Natural Science), 2019, 38(06): 1-5.

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基于自适应均值的地铁隧道裂缝图像滤波算法

Image Filtering Algorithm for Subway Tunnel Cracks Based on Self-adaptive Mean Hardware-in-the-loop Simulation

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