基于分数阶傅里叶变换的隧道低对比度裂缝检测
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摘要
隧道结构与环境的复杂性,使得隧道裂缝的检测环境存在较多干扰,如混凝土泥浆、污渍、渗水的面积较大,与细小裂缝的对比度较低,利用常规的形态学方法很容易漏检裂缝。为解决此问题,提出一种基于分数阶傅里叶变换的裂缝检测方法。该方法通过分数阶傅里叶变换将裂缝图像映射到时频域空间,实现信号的时频域展开;同时不同阶次的分数阶傅里叶变换对应不同的时频域,有利于提取裂缝图像中的污渍特征,通过背景信息补偿污渍区域,均衡了图像背景的对比度;结合分数阶微积分方法对图像进行增强,最后利用连通域方法提取裂缝。实验结果表明,提出的方法能够有效去除隧道壁污渍,检测出低对比度的隧道裂缝。
Due to the complexity of tunnel structure and environment,strong interference exists in the detection environment of tunnel cracks,such as concrete mud,dirt,water seepage area,etc..This results in low contrast between background and small cracks.Therefore,it is easy to miss cracks by using conventional morphological methods.In order to solve this problem,this paper proposed a crack detection method based on fractional Fourier transform.In this me-thod,the image is mapped to different time-frequency domains by different order fractional Fourier transform,which is helpful to extract the filth feature in the crack image.The background contrast of the image is balanced by compensating the filth region with the background information.The fractional differential method is used to enhance the image and the connected domain method is used to extract the cracks.Experimental results show that the proposed method can effectively remove the filth region and detect tunnel cracks with low contrast.
Due to the complexity of tunnel structure and environment,strong interference exists in the detection environment of tunnel cracks,such as concrete mud,dirt,water seepage area,etc..This results in low contrast between background and small cracks.Therefore,it is easy to miss cracks by using conventional morphological methods.In order to solve this problem,this paper proposed a crack detection method based on fractional Fourier transform.In this me-thod,the image is mapped to different time-frequency domains by different order fractional Fourier transform,which is helpful to extract the filth feature in the crack image.The background contrast of the image is balanced by compensating the filth region with the background information.The fractional differential method is used to enhance the image and the connected domain method is used to extract the cracks.Experimental results show that the proposed method can effectively remove the filth region and detect tunnel cracks with low contrast.
引文
[1] 白彪.地铁隧道表面图像裂缝识别算法研究[D].北京:北京交通大学,2015.
[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(S1):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] YANG X,BALEANU D,SRIVASTAVA H M.3-Local Frac- tional Fourier Transform and Applications[M].Elsevier Ltd,2016:95-145.
[9] 王亚星.基于分数阶傅里叶变换的人脸识别[D].郑州:郑州大学,2015.
[10] 孟祥意,陶然,王越.分数阶傅里叶域循环多抽样率信号处理理论[J].中国科学(E辑:技术科学),2009,39(5):1004-1015.
[11] 李博.谢巍.基于自适应分数阶微积分的图像去噪与增强算法[J].系统工程与电子技术,2016,38(1):185-192.
[12] 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(S1):259-261.
[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(S1):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] YANG X,BALEANU D,SRIVASTAVA H M.3-Local Frac- tional Fourier Transform and Applications[M].Elsevier Ltd,2016:95-145.
[9] 王亚星.基于分数阶傅里叶变换的人脸识别[D].郑州:郑州大学,2015.
[10] 孟祥意,陶然,王越.分数阶傅里叶域循环多抽样率信号处理理论[J].中国科学(E辑:技术科学),2009,39(5):1004-1015.
[11] 李博.谢巍.基于自适应分数阶微积分的图像去噪与增强算法[J].系统工程与电子技术,2016,38(1):185-192.
[12] 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(S1):259-261.