基于图像增强与分水岭分割的隧道低对比度裂缝提取方法

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 259-261.

基于图像增强与分水岭分割的隧道低对比度裂缝提取方法

周丽军

山西省交通科学研究院山西省公路智能监测工程技术研究中心太原030000

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:周丽军(1984-),女,博士,工程师,主要研究方向为路桥隧道检测,E-mail:zhoulj2012@hotmail.com。
基金资助:
山西省自然科学基金项目(2015021126),山西省交通运输厅科研项目(2017-1-25)资助

Low-contrast Crack Extraction Method Based on Image Enhancement and Watershed Segmentation

ZHOU Li-jun

Shanxi Engineering Research Center for Road Intelligent Monitoring,Shanxi Transportation Research Institute,Taiyuan 030000,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 在实际的隧道裂缝检测中,存在细小、对比度低且有污渍点干扰的隧道裂缝,利用常规方法很容易漏检裂缝。为了解决此问题,提出一种基于图像增强与分水岭分割的裂缝提取算法,该算法有效利用背景信息补偿了污渍点,均衡了图像背景对比度。结合高低帽变换方法对图像进行增强,然后根据分水岭算法获取分水岭分割线;比较分割线所在位置的灰度值与其周边灰度值,并通过灰度值差异判断裂缝边缘,从而提取裂缝。实验结果表明,所提算法能够准确、有效地检测出完整的隧道裂缝,且对噪声具有鲁棒性。

关键词: 低对比度, 分水岭, 高低帽变换, 裂缝检测, 图像增强

Abstract: In the process of tunnel crack detection in actual scene,there exists small,low-contrast and stain-interfered cracks.It is difficult to extract those cracks by conventional methods.In order to solve this problem,a crack detection method based on image enhancement and watershed segmentation was proposed.In this method,the interfered stain is removed to balance the image background contrast.The image is further enhanced by top-hat and bottom-hat transformation.Then the segmentation lines are obtained by watershed algorithm.According to the gray-value difference between the gray-value of segmentation line and its surrounding gray-value,the crack edge can be extracted.Experimental results show that the proposed method is accurate and effective to detect tunnel cracks and it is also robust to noise.

Key words: Crack detection, Image enhancement, Low-contrast, Top-hat/bottom-hat transformation, Watershed

中图分类号:

TP391.41

周丽军. 基于图像增强与分水岭分割的隧道低对比度裂缝提取方法[J]. 计算机科学, 2018, 45(6A): 259-261. https://doi.org/

ZHOU Li-jun. Low-contrast Crack Extraction Method Based on Image Enhancement and Watershed Segmentation[J]. Computer Science, 2018, 45(6A): 259-261. https://doi.org/

参考文献

[1]仇玉良.隧道检测监测技术及信息化智能管理系统[M].北京:人民交通出版社,2013:25-56.
[2]朱力强,白彪,王耀东,等.基于特征分析的地铁隧道裂缝识别算法[J].铁道学报,2015,37(5):64-70.
[3]瞿中,林丽丹,郭阳.形态学与区域延伸相结合的图像裂缝检测算法研究[J].计算机科学,2014,41(11):297-300.
[4]杨松,邵龙潭,郭晓霞,等.基于骨架和分形的混凝土裂缝图像识别算法[J].仪器仪表学报,2012,33(8):1850-1855.
[5]王世芳,车艳丽,李楠,等.一种基于多尺度脊边缘的沥青路面裂缝检测算法[J].中国公路学报,2017,30(4):32-41.
[6]李锋,阚建霞.基于Sobel算子的图像快速二维最大熵阈值分割算法[J].计算机科学,2015,42(6A):209-210.
[7]赵志刚,赵祥模,宋焕生,等.基于直方图估计和形状分析的沥青路面裂缝识别算法[J].仪器仪表学报,2010,31(10):2260-2266.
[8]舒速,杨明.基于分水岭分割和稀疏表示的高光谱图像分类方法[J].计算机科学,2016,43(2):89-93.
[9]张博,何彬彬.改进的分水岭变换算法在高分辨率遥感影像多尺度分割中的应用[J].地球信息科学学报,2014,16(1):142-149.
[10]TARABALKA Y,CHANUSSOT J,BENEDIKTSSON A.Segmentation and Classification of Hyperspectral Images using Watershed Transformation[J].Pattern Recognition,2010,43(7):2367-2379.
[11]ACHARJYA P.GHOSHAL D.Watershed Segmentation based on Distance Transform and Edge Detection Techniques[J].International Journal of Computer Applications,2012,52(13):6-10.
[12]余烨,李冰飞,张小魏,等.面向RGBD图像的标记分水岭分割[J].中国图象图形学报,2016,21(2):145-154.

相关文章 15

[1]	王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰. 基于多源迁移学习的大坝裂缝检测 Dam Crack Detection Based on Multi-source Transfer Learning 计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124
[2]	赵明华, 周童童, 都双丽, 石争浩. 基于虚拟曝光方法的单幅逆光图像增强 Single Backlit Image Enhancement Based on Virtual Exposure Method 计算机科学, 2022, 49(6A): 384-389. https://doi.org/10.11896/jsjkx.210400243
[3]	赵征鹏, 李俊钢, 普园媛. 基于卷积神经网络的Retinex低照度图像增强 Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network 计算机科学, 2022, 49(6): 199-209. https://doi.org/10.11896/jsjkx.210400092
[4]	瞿中, 陈雯. 基于空洞卷积和多特征融合的混凝土路面裂缝检测 Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion 计算机科学, 2022, 49(3): 192-196. https://doi.org/10.11896/jsjkx.210100164
[5]	祝一帆, 王海涛, 李可, 吴贺俊. 一种高精度路面裂缝检测网络结构:Crack U-Net Crack U-Net:Towards High Quality Pavement Crack Detection 计算机科学, 2022, 49(1): 204-211. https://doi.org/10.11896/jsjkx.210100128
[6]	黄雪冰, 魏佳艺, 沈文宇, 凌力. 基于自适应加权重复值滤波和同态滤波的MR图像增强 MR Image Enhancement Based on Adaptive Weighted Duplicate Filtering and Homomorphic Filtering 计算机科学, 2021, 48(6A): 21-27. https://doi.org/10.11896/jsjkx.200800183
[7]	杨秀璋, 武帅, 夏换, 于小民. 基于自适应图像增强技术的水族文字提取与识别研究 Research on Shui Characters Extraction and Recognition Based on Adaptive Image Enhancement Technology 计算机科学, 2021, 48(6A): 74-79. https://doi.org/10.11896/jsjkx.200900070
[8]	瞿中, 谢钇. 基于全U网络的混凝土路面裂缝检测算法 Concrete Pavement Crack Detection Algorithm Based on Full U-net 计算机科学, 2021, 48(4): 187-191. https://doi.org/10.11896/jsjkx.200100113
[9]	李昌兴, 雷柳, 张晓璐. 基于形态学图像增强和PCNN的脑部CT与MRI图像融合 Brain CT and MRI Image Fusion Based on Morphological Image Enhancement and PCNN 计算机科学, 2020, 47(10): 194-199. https://doi.org/10.11896/jsjkx.190700185
[10]	徐敏敏, 寇光杰, 马云艳, 岳峻, 贾世祥, 张志旺. 基于PCNN内部活动项的彩色图像增强算法 Color Image Enhancement Algorithm Based on PCNN Internal Activities 计算机科学, 2019, 46(6A): 259-262.
[11]	周丽军, 刘晓. 基于分数阶傅里叶变换的隧道低对比度裂缝检测 Low-contrast Crack Detection Method Based on Fractional Fourier Transform 计算机科学, 2019, 46(6A): 208-210.
[12]	姚哲维, 杨丰, 黄靖, 刘娅琴. 改进型循环生成对抗网络的血管内超声图像增强 Improved CycleGANs for Intravascular Ultrasound Image Enhancement 计算机科学, 2019, 46(5): 221-227. https://doi.org/10.11896/j.issn.1002-137X.2019.05.034
[13]	王丽苹, 高瑞贞, 张京军, 王二成. 基于卷积神经网络的混凝土路面裂缝检测 Crack Detection of Concrete Pavement Based on Convolutional Neural Network 计算机科学, 2019, 46(11A): 584-589.
[14]	杨秀璋, 夏换, 于小民. 一种基于水族濒危文字的图像增强及识别方法 Image Enhancement and Recognition Method Based on Shui-characters 计算机科学, 2019, 46(11A): 324-328.
[15]	张玉雪,唐振民,钱彬,徐威. 基于稀疏表示和多特征融合的路面裂缝检测 Pavement Crack Detection Based on Sparse Representation and Multi-feature Fusion 计算机科学, 2018, 45(7): 271-277. https://doi.org/10.11896／j.issn.1002-137X.2018.07.047

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed