高等级公路路面病害自动检测方法研究
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摘要
本文结合吉林省科技厅高新技术项目“路基路面智能集成检测车”,研究了基于图像处理的沥青公路路面裂缝病害检测方法,并开发了检测软件。
本文综述了公路路面病害检测技术的研究现状,并对沥青公路路面破损及其检测方法进行了介绍。采用平稳小波阈值去噪方法,对实际采集的路面病害图像进行了去噪处理,利用侧抑制原理对去噪后的图像进行了图像增强,图像的质量得到了明显的改善。基于模糊C-均值聚类和最大类间方差法,提出了一种自适应阈值图像分割方法,对路面病害图像进行了处理。设计了BP神经网络识别系统,对分割处理后的病害图像进行识别,实验表明该网络能够较好的对路面裂缝进行识别。
本文的研究结果可直接应用于公路路面的破损检测及病害识别,也可为其他领域的图像分析研究提供参考。
The road is critical infrastructure to support national economic development and social progress, and it holds the important status in the national economy and the lives of the people. As the rapid developing of road construction and the consummating of road network system, the road maintenance management is becoming increasingly important. The detection of road condition which is an important part of the road maintenance management is facing higher requirements. The traditional manual methods can’t meet the need of pavement’s development now, because they have the following shortcomings such as low efficiency, affecting transportation and non-precision. How to realize accurately, rapidly, non-destructively road condition detecting is the developing direction.
Pavement crack which is the main form of the pavement surface distress is the key detection project. Pavement cracks which are in a variety of forms are the key point and difficulty in pavement surface distress detection. At present, many researches on the pavement surface distress detection are focused on it. Depending on the high-tech project of Jilin Provincial Science&Technology Department–“The Integrated Detection Vehicle of subgrade and Pavement”, this thesis studied pavement crack distress detection methods which are based on image processing. In this thesis, stationary wavelet thresholding method on the pavement image denoising is proposed, then, lateral inhibition principle is used for the image enhancing. An adaptive thresholding segmentation method based on the fuzzy c-means clustering and Otsu law is used in the road image segmentation processing. Pavement Cracks are identified by the BP neural network method. The main content of this thesis summarized as follows:
(1) It introduces the status and development trends of the technology of pavement surface distress detection on the basis of referring to domestic and foreign literature.
(2) It briefly introduces the asphalt road surface damaged knowledge, including the types of road damage, the causes, characteristics and hazards of several types of cracks distress, the evaluation methods of road damaged condition and pavement condition index PCI calculation method etc. In addition, the thesis also introduces the imaging characteristics of the pavement image.
(3) Stationary wavelet thresholding method on the pavement image denoising is proposed in the thesis. This method is very effective by comparing with median filter, neighborhood weighted mean filter and DWT thresholding method. After image denoising, lateral inhibition principle is used for the image enhancing. The crack distress information is enhanced.
(4) An adaptive thresholding segmentation method based on the fuzzy c means clustering and Otsu law is used in the road image segmentation processing. Experimental results show that this method is effective than widely used Otsu law. The paper uses a method that extracts information in crack seed image which is built according to the direction template of crack type.
(5) BP network is simple and practical, widely used in pattern recognition, classification, function approximation, data compression etc.. The paper brings a pavement crack image type classification method based on BP neural network using domestic and foreign studying results for reference. We design a three-layer BP neural network using six features extracting from crack seed image as the input and five type of pavement crack as the output. These five features are transverse crack, longitudinal crack, alligator crack, block crack and no crack. In order to train and evaluate the network, we collected 150 and 100 samples images as the training set and test set respectively. The results show that the network can be used for identificating pavement cracks, and the overall recognition rate over 90%.
(6) The integrated detection testing platform of subgrade and pavement is introduced, including components, processes and the composition of some subsystems. In the final, the main reseach of this thesis is summarized, and the subsequent research work is presented.
This thesis studies on the high-grade asphalt road surface crack detection. Some of methods used in this thesis are novel in theory and feasible in practice. The author hopes that this paper is helpful to the follow-up research.
本文综述了公路路面病害检测技术的研究现状,并对沥青公路路面破损及其检测方法进行了介绍。采用平稳小波阈值去噪方法,对实际采集的路面病害图像进行了去噪处理,利用侧抑制原理对去噪后的图像进行了图像增强,图像的质量得到了明显的改善。基于模糊C-均值聚类和最大类间方差法,提出了一种自适应阈值图像分割方法,对路面病害图像进行了处理。设计了BP神经网络识别系统,对分割处理后的病害图像进行识别,实验表明该网络能够较好的对路面裂缝进行识别。
本文的研究结果可直接应用于公路路面的破损检测及病害识别,也可为其他领域的图像分析研究提供参考。
The road is critical infrastructure to support national economic development and social progress, and it holds the important status in the national economy and the lives of the people. As the rapid developing of road construction and the consummating of road network system, the road maintenance management is becoming increasingly important. The detection of road condition which is an important part of the road maintenance management is facing higher requirements. The traditional manual methods can’t meet the need of pavement’s development now, because they have the following shortcomings such as low efficiency, affecting transportation and non-precision. How to realize accurately, rapidly, non-destructively road condition detecting is the developing direction.
Pavement crack which is the main form of the pavement surface distress is the key detection project. Pavement cracks which are in a variety of forms are the key point and difficulty in pavement surface distress detection. At present, many researches on the pavement surface distress detection are focused on it. Depending on the high-tech project of Jilin Provincial Science&Technology Department–“The Integrated Detection Vehicle of subgrade and Pavement”, this thesis studied pavement crack distress detection methods which are based on image processing. In this thesis, stationary wavelet thresholding method on the pavement image denoising is proposed, then, lateral inhibition principle is used for the image enhancing. An adaptive thresholding segmentation method based on the fuzzy c-means clustering and Otsu law is used in the road image segmentation processing. Pavement Cracks are identified by the BP neural network method. The main content of this thesis summarized as follows:
(1) It introduces the status and development trends of the technology of pavement surface distress detection on the basis of referring to domestic and foreign literature.
(2) It briefly introduces the asphalt road surface damaged knowledge, including the types of road damage, the causes, characteristics and hazards of several types of cracks distress, the evaluation methods of road damaged condition and pavement condition index PCI calculation method etc. In addition, the thesis also introduces the imaging characteristics of the pavement image.
(3) Stationary wavelet thresholding method on the pavement image denoising is proposed in the thesis. This method is very effective by comparing with median filter, neighborhood weighted mean filter and DWT thresholding method. After image denoising, lateral inhibition principle is used for the image enhancing. The crack distress information is enhanced.
(4) An adaptive thresholding segmentation method based on the fuzzy c means clustering and Otsu law is used in the road image segmentation processing. Experimental results show that this method is effective than widely used Otsu law. The paper uses a method that extracts information in crack seed image which is built according to the direction template of crack type.
(5) BP network is simple and practical, widely used in pattern recognition, classification, function approximation, data compression etc.. The paper brings a pavement crack image type classification method based on BP neural network using domestic and foreign studying results for reference. We design a three-layer BP neural network using six features extracting from crack seed image as the input and five type of pavement crack as the output. These five features are transverse crack, longitudinal crack, alligator crack, block crack and no crack. In order to train and evaluate the network, we collected 150 and 100 samples images as the training set and test set respectively. The results show that the network can be used for identificating pavement cracks, and the overall recognition rate over 90%.
(6) The integrated detection testing platform of subgrade and pavement is introduced, including components, processes and the composition of some subsystems. In the final, the main reseach of this thesis is summarized, and the subsequent research work is presented.
This thesis studies on the high-grade asphalt road surface crack detection. Some of methods used in this thesis are novel in theory and feasible in practice. The author hopes that this paper is helpful to the follow-up research.
引文
[1] 中华人民共和国交通部. 国家高速公路网规划[EB/OL]. http://www.moc.gov.cn,2006.11.20
[2] Fukuhara. Automatic Pavement Distress Survey System[J]. ASCE Journal of Transportation. Engineering, 1990.06, 116(3):280-286
[3] 李强、潘玉利. 路面快速检测技术与设备研究进展及分析[J]. 公路交通科技,2005.09,22(9):35-39
[4] Kelvin C. P. Wang, Robert P. Elliott. Investigation of archiving for pavement surface distress survey[R]. A final report submitted to Mack-Blackwell Transportation Center, 1999.07
[5] The Washington State Department of Transportation. WSDOT Pavement Guide of Pavement Evaluation[EB/OL]. http://training.ce.washington.edu/WSDOT/, 2002
[6] Maerz, N. H., McKenna S.. Mobile Highway Inventory And Measurement System[J]. Transportation Research Record, 1999, No.1690, pp.135-142
[7] 高建贞. 基于图像分析的道路病害自动检测研究[D]. 南京:南京理工大学,2003
[8] Tarek A. Monem, Amr A. Oloufa, Hesham Mahgoub. Asphalt Crack Detection Using Thermography[C]. InfraMation 2005 Proceedings, 2005.06, ITC 108 A
[9] 潘玉利. 高速公路现代化管理技术的研究[J]. 公路交通科技,2005.09,22(4)
[10] Chau Lee, C.F. Chen, Shu-Meng Huang, Chun-Jung Hsu, and Shu-Wang Sheu. Development of An Automated Airport Pavement Image Collection System[J]. Journal of Marine Science and Technology, 2002, 10(1):1-7
[11] 王荣本、王超、初秀民. 路面破损图像识别研究进展[J]. 吉林大学学报(工学版),2002.10,32(4):91-97
[12] 张全升、荆根强、杨峰. 高速公路路面破损与路基病害三维一体化检测技术研究[J]. 公路交通科技(应用技术版),2006.09,pp.37-41
[13] 张全升、谷乾龙、荆根强. 新型公路智能检测系统的设计与应用[J]. 公路交通科技(应用技术版),2006.08,pp.32-34
[14] 盛安连. 路基路面检测技术[M]. 北京:人民交通出版社,1996.12
[15] 常魁和、高群. 公路沥青路面养护新技术[M]. 北京:人民交通出版社,2001
[16] Transportation Research Board the National Academies. Automated Pavement Distress Collection Techniques[C]. Washington D.C.:Transportation Research Board, 2004
[17] 李晋惠、楼伟、姜寿山. 基于 CCD 的公路路面病害检测技术研究[J]. 西安工业学院学报,2002.06,22(2):95-99
[18] Bugao Xu and Yaxiong Huang. Development of an Automatic Pavement Surface Distress Inspection System[R]. The University of Texas at Austin, CTR Research Report:7-4975-1, Center for Transportation Research, 2003
[19] Alasdair McAndrew and Anne Venables. A“Secondary”Look at Digital Image Processing[R]. Technical Symposium on Computer Science Education, 2005, pp.337-341
[20] 阮秋琦. 数字图像处理学[M]. 北京:电子工业出版社,2001.01
[21] Rafael C. Gonzalez and Richard E. Woods 著,阮秋琦、阮宇智等译. 数字图像处理(第二版)[M]. 北京:电子工业出版社,2003
[22] 王华. 基于分形理论的路面裂缝检测技术的研究[D]. 哈尔滨:哈尔滨工业大学,2004
[23] 范立南、徐心和. 基于不变矩特征和神经网络的图像模式模糊分类[J]. 东北大学学报(自然科学版),2004.08,25(8):738-741
[24] 肖旺新、张雪. 小波神经网络在基于图像处理的路面破损识别中的应用[J]. 公路交通技术,2005.08,No.4,pp.45-48
[25] S. Paquis, V. Legeay, H. Konik, J. Charrier. Multiresolution Texture Analysis applied to Road Surface Inspection[J]. SPIE, 1999, vol.3652, pp.242-245
[26] 潘玉利. 路面管理系统原理[M]. 北京:人民交通出版社,1998
[27] The Washington Asphalt Pavement Association. Asphalt Pavement Guide of Surface Distresses[EB/OL]. http://www.asphaltwa.com, 2002
[28] 陈传德. 高速公路养护管理[M]. 北京:人民交通出版社,2005
[29] 刘玉臣. 基于模糊聚类方法的路面破损识别[D]. 长春:吉林大学,2005
[30] 刘小光. 沥青高速公路路面检测与破损率识别技术研究及系统实现[D]. 长春:吉林大学,2006
[31] 李晋惠. 用图像处理的方法检测公路路面裂缝类病害[J]. 长安大学学报(自然科学版),2004.05,24(3):24-29
[32] 孙延奎. 小波分析及其应用[M]. 北京:机械工业出版社,2005.10
[33] 高清维、李斌、解光军、庄镇泉. 基于平稳小波变换的图像去噪方法[J]. 计算机研究与发展,2002.12,39(12)
[34] Hui Cheng, Qiuze Yu, Jinwen Tian, Jian Liu. Image Denoising Using Wavelet and Support Vector Regression[R]. Proceedings of the Third International Conference on Image and Graphics (ICIG’04), IEEE Computer Society, 2004.12, pp.43-46
[35] 飞思科技产品研发中心. 小波分析理论与 MATLAB 7 实现[M]. 北京:电子工业出版社,2005.03
[36] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins 著,阮秋琦等译. 数字图像处理(MATLAB 版)[M]. 北京:电子工业出版社,2005.09
[37] Stéphane Mallat(法)著,杨力华、戴道清、黄文良、湛秋辉译. 信号处理的小波导引[M]. 北京:机械工业出版社,2002.09
[38] 杨建国著. 小波分析及其工程应用[M]. 北京:机械工业出版社,2005.07
[39] David L. Donoho. Denoising by soft thresholding[J]. IEEE Trans. on Information Theory, 1995, 41(3):613-627
[40] 王红梅、李言俊、张科. 基于平稳小波变换的图像去噪方法[J]. 红外技术,2006.07,28(7):404-407
[41] 李士心、刘鲁源、杨晔、陈刚. 基于平稳小波变换的陀螺仪信号去噪方法[J]. 天津大学学报,2003.03,36(2):165-168
[42] 徐元柳、张晓帆、樊永峰. 一种基于平稳小波域的图像去噪新方法[J]. 中国科技信息,2005.06,No.11,pp.17
[43] Wang XH, Istepanian RS, Song YH. Microarray image enhancement by denoising using stationary wavelet transform[J]. IEEE Trans. Nanobioscience, 2003.12, 2(4):184-189
[44] 崔华、宋国乡. 基于小波阈值去噪方法的一种改进方案[J]. 现代电子技术,2005.01,28(1):8-10
[45] Kelvin C. P. Wang and Weiguo Gong. Real-Time Automated Survey System of Pavement Cracking in Parallel Environment[J]. Journal of Infrastructure Systems ? ASCE, 2005.09, pp.154-164
[46] Egemen Teomete, Viren R. Amin, Halil Ceylan and Omar Smadi. Digital Image Processing for Pavement Distress Analyses[A]. Proceedings of the 2005 Mid-Continent Transpo_ rtation Research Symposium, Ames, Iowa, 2005.08
[47] H. D. Cheng and M. Miyojim. Novel System for Automatic Pavement Distress Detection[J]. Journal of Computing in Civil Engineering, 1998, No.7, pp.145-152
[48] 王立、张科、李言俊. 视网膜皮层图像信号处理的传输机制与仿真[J]. 光学技术,2003.07,29(4):420-422
[49] 赵大炜、訾方、张科、李言俊. 基于侧抑制网络的红外图像预处理[J]. 弹箭与制导学报,2005.11,25(4):213-215, 218
[50] M. A. Kim Paradis and M. E. Jemigan. Homomorphic vs. Multiplicative Lateral Inhibition Models for Image Enhancement[A]. 1994 IEEE International Conference on Systems, Man, and Cybernetics(IEEE SMC 1994), 1994.10, 1(1):286-291
[51] 冯晓毅、刘诗斌. 图像边缘增强的多分辨率侧抑制网络方法[J]. 计算技术与自动化,2003.06,22(2):97-99
[52] Yu Ruixing,Li Yanjun, Zhang Ke. Infrared Image Edge Detection Using Improved Bubble Function[J]. Infrared Technology, 2006.01, 28(1):36-38
[53] 王保平. 基于模糊技术的图像处理方法研究[D]. 西安:西安电子科技大学,2004
[54] 王茜蒨、彭中、刘莉. 一种基于自适应阈值的图像分割算法[J]. 北京理工大学学报,2003.08,23(4):521-524
[55] 章毓晋著. 图象分割[M]. 北京:科学出版社,2001.02
[56] 章毓晋编著. 图像工程.上册 图像处理(第二版)[M]. 北京:清华大学出版社,2006.03
[57] 崔屹编著. 图象处理与分析-数学形态学方法及应用[M]. 北京:科学出版社,2000.04
[58] Byoung Jik Lee. Development of an Integrated Digital Pavement Imaging and Neural Network System[D]. Iowa, USA: The University of Iowa, 2001.12
[59] Chun-Jung Hsu, Chi-Fran Chen, Chau Lee, Shu-Meng Huang. Airport Pavement Distress Image Classification Using Moment Invariant Neural Network[C]. Proc. ACRS 2001-22nd Asian Conference on Remote Sensing, 2001.11, Singapore, Vol.1, pp.216-220
[60] 李士勇编著. 模糊控制?神经控制和智能控制论(第二版)[M]. 哈尔滨:哈尔滨工业大学出版,1998.9
[61] 袁曾任编著. 人工神经网络及其应用[M]. 北京:清华大学出版社、南宁:广西科学技术大学出版社,1999.10
[62] 周志华、曹存根. 神经网络及其应用[M]. 北京:清华大学出版社,2004.9
[63] 何玉彬、李新忠著. 神经网络控制技术及其应用[M]. 北京:科学出版社,2000.11
[64] 飞思科技产品研发中心编著. MATLAB6.5 辅助神经网络分析与设计[M]. 北京:电子工业出版社,2003.01
[65] 张斌、严骏、毛宁. BP 神经网络在挖掘机电气系统故障诊断中的应用[J]. 土石方机械与施工技术,2005,No.9,pp.49-50
[2] Fukuhara. Automatic Pavement Distress Survey System[J]. ASCE Journal of Transportation. Engineering, 1990.06, 116(3):280-286
[3] 李强、潘玉利. 路面快速检测技术与设备研究进展及分析[J]. 公路交通科技,2005.09,22(9):35-39
[4] Kelvin C. P. Wang, Robert P. Elliott. Investigation of archiving for pavement surface distress survey[R]. A final report submitted to Mack-Blackwell Transportation Center, 1999.07
[5] The Washington State Department of Transportation. WSDOT Pavement Guide of Pavement Evaluation[EB/OL]. http://training.ce.washington.edu/WSDOT/, 2002
[6] Maerz, N. H., McKenna S.. Mobile Highway Inventory And Measurement System[J]. Transportation Research Record, 1999, No.1690, pp.135-142
[7] 高建贞. 基于图像分析的道路病害自动检测研究[D]. 南京:南京理工大学,2003
[8] Tarek A. Monem, Amr A. Oloufa, Hesham Mahgoub. Asphalt Crack Detection Using Thermography[C]. InfraMation 2005 Proceedings, 2005.06, ITC 108 A
[9] 潘玉利. 高速公路现代化管理技术的研究[J]. 公路交通科技,2005.09,22(4)
[10] Chau Lee, C.F. Chen, Shu-Meng Huang, Chun-Jung Hsu, and Shu-Wang Sheu. Development of An Automated Airport Pavement Image Collection System[J]. Journal of Marine Science and Technology, 2002, 10(1):1-7
[11] 王荣本、王超、初秀民. 路面破损图像识别研究进展[J]. 吉林大学学报(工学版),2002.10,32(4):91-97
[12] 张全升、荆根强、杨峰. 高速公路路面破损与路基病害三维一体化检测技术研究[J]. 公路交通科技(应用技术版),2006.09,pp.37-41
[13] 张全升、谷乾龙、荆根强. 新型公路智能检测系统的设计与应用[J]. 公路交通科技(应用技术版),2006.08,pp.32-34
[14] 盛安连. 路基路面检测技术[M]. 北京:人民交通出版社,1996.12
[15] 常魁和、高群. 公路沥青路面养护新技术[M]. 北京:人民交通出版社,2001
[16] Transportation Research Board the National Academies. Automated Pavement Distress Collection Techniques[C]. Washington D.C.:Transportation Research Board, 2004
[17] 李晋惠、楼伟、姜寿山. 基于 CCD 的公路路面病害检测技术研究[J]. 西安工业学院学报,2002.06,22(2):95-99
[18] Bugao Xu and Yaxiong Huang. Development of an Automatic Pavement Surface Distress Inspection System[R]. The University of Texas at Austin, CTR Research Report:7-4975-1, Center for Transportation Research, 2003
[19] Alasdair McAndrew and Anne Venables. A“Secondary”Look at Digital Image Processing[R]. Technical Symposium on Computer Science Education, 2005, pp.337-341
[20] 阮秋琦. 数字图像处理学[M]. 北京:电子工业出版社,2001.01
[21] Rafael C. Gonzalez and Richard E. Woods 著,阮秋琦、阮宇智等译. 数字图像处理(第二版)[M]. 北京:电子工业出版社,2003
[22] 王华. 基于分形理论的路面裂缝检测技术的研究[D]. 哈尔滨:哈尔滨工业大学,2004
[23] 范立南、徐心和. 基于不变矩特征和神经网络的图像模式模糊分类[J]. 东北大学学报(自然科学版),2004.08,25(8):738-741
[24] 肖旺新、张雪. 小波神经网络在基于图像处理的路面破损识别中的应用[J]. 公路交通技术,2005.08,No.4,pp.45-48
[25] S. Paquis, V. Legeay, H. Konik, J. Charrier. Multiresolution Texture Analysis applied to Road Surface Inspection[J]. SPIE, 1999, vol.3652, pp.242-245
[26] 潘玉利. 路面管理系统原理[M]. 北京:人民交通出版社,1998
[27] The Washington Asphalt Pavement Association. Asphalt Pavement Guide of Surface Distresses[EB/OL]. http://www.asphaltwa.com, 2002
[28] 陈传德. 高速公路养护管理[M]. 北京:人民交通出版社,2005
[29] 刘玉臣. 基于模糊聚类方法的路面破损识别[D]. 长春:吉林大学,2005
[30] 刘小光. 沥青高速公路路面检测与破损率识别技术研究及系统实现[D]. 长春:吉林大学,2006
[31] 李晋惠. 用图像处理的方法检测公路路面裂缝类病害[J]. 长安大学学报(自然科学版),2004.05,24(3):24-29
[32] 孙延奎. 小波分析及其应用[M]. 北京:机械工业出版社,2005.10
[33] 高清维、李斌、解光军、庄镇泉. 基于平稳小波变换的图像去噪方法[J]. 计算机研究与发展,2002.12,39(12)
[34] Hui Cheng, Qiuze Yu, Jinwen Tian, Jian Liu. Image Denoising Using Wavelet and Support Vector Regression[R]. Proceedings of the Third International Conference on Image and Graphics (ICIG’04), IEEE Computer Society, 2004.12, pp.43-46
[35] 飞思科技产品研发中心. 小波分析理论与 MATLAB 7 实现[M]. 北京:电子工业出版社,2005.03
[36] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins 著,阮秋琦等译. 数字图像处理(MATLAB 版)[M]. 北京:电子工业出版社,2005.09
[37] Stéphane Mallat(法)著,杨力华、戴道清、黄文良、湛秋辉译. 信号处理的小波导引[M]. 北京:机械工业出版社,2002.09
[38] 杨建国著. 小波分析及其工程应用[M]. 北京:机械工业出版社,2005.07
[39] David L. Donoho. Denoising by soft thresholding[J]. IEEE Trans. on Information Theory, 1995, 41(3):613-627
[40] 王红梅、李言俊、张科. 基于平稳小波变换的图像去噪方法[J]. 红外技术,2006.07,28(7):404-407
[41] 李士心、刘鲁源、杨晔、陈刚. 基于平稳小波变换的陀螺仪信号去噪方法[J]. 天津大学学报,2003.03,36(2):165-168
[42] 徐元柳、张晓帆、樊永峰. 一种基于平稳小波域的图像去噪新方法[J]. 中国科技信息,2005.06,No.11,pp.17
[43] Wang XH, Istepanian RS, Song YH. Microarray image enhancement by denoising using stationary wavelet transform[J]. IEEE Trans. Nanobioscience, 2003.12, 2(4):184-189
[44] 崔华、宋国乡. 基于小波阈值去噪方法的一种改进方案[J]. 现代电子技术,2005.01,28(1):8-10
[45] Kelvin C. P. Wang and Weiguo Gong. Real-Time Automated Survey System of Pavement Cracking in Parallel Environment[J]. Journal of Infrastructure Systems ? ASCE, 2005.09, pp.154-164
[46] Egemen Teomete, Viren R. Amin, Halil Ceylan and Omar Smadi. Digital Image Processing for Pavement Distress Analyses[A]. Proceedings of the 2005 Mid-Continent Transpo_ rtation Research Symposium, Ames, Iowa, 2005.08
[47] H. D. Cheng and M. Miyojim. Novel System for Automatic Pavement Distress Detection[J]. Journal of Computing in Civil Engineering, 1998, No.7, pp.145-152
[48] 王立、张科、李言俊. 视网膜皮层图像信号处理的传输机制与仿真[J]. 光学技术,2003.07,29(4):420-422
[49] 赵大炜、訾方、张科、李言俊. 基于侧抑制网络的红外图像预处理[J]. 弹箭与制导学报,2005.11,25(4):213-215, 218
[50] M. A. Kim Paradis and M. E. Jemigan. Homomorphic vs. Multiplicative Lateral Inhibition Models for Image Enhancement[A]. 1994 IEEE International Conference on Systems, Man, and Cybernetics(IEEE SMC 1994), 1994.10, 1(1):286-291
[51] 冯晓毅、刘诗斌. 图像边缘增强的多分辨率侧抑制网络方法[J]. 计算技术与自动化,2003.06,22(2):97-99
[52] Yu Ruixing,Li Yanjun, Zhang Ke. Infrared Image Edge Detection Using Improved Bubble Function[J]. Infrared Technology, 2006.01, 28(1):36-38
[53] 王保平. 基于模糊技术的图像处理方法研究[D]. 西安:西安电子科技大学,2004
[54] 王茜蒨、彭中、刘莉. 一种基于自适应阈值的图像分割算法[J]. 北京理工大学学报,2003.08,23(4):521-524
[55] 章毓晋著. 图象分割[M]. 北京:科学出版社,2001.02
[56] 章毓晋编著. 图像工程.上册 图像处理(第二版)[M]. 北京:清华大学出版社,2006.03
[57] 崔屹编著. 图象处理与分析-数学形态学方法及应用[M]. 北京:科学出版社,2000.04
[58] Byoung Jik Lee. Development of an Integrated Digital Pavement Imaging and Neural Network System[D]. Iowa, USA: The University of Iowa, 2001.12
[59] Chun-Jung Hsu, Chi-Fran Chen, Chau Lee, Shu-Meng Huang. Airport Pavement Distress Image Classification Using Moment Invariant Neural Network[C]. Proc. ACRS 2001-22nd Asian Conference on Remote Sensing, 2001.11, Singapore, Vol.1, pp.216-220
[60] 李士勇编著. 模糊控制?神经控制和智能控制论(第二版)[M]. 哈尔滨:哈尔滨工业大学出版,1998.9
[61] 袁曾任编著. 人工神经网络及其应用[M]. 北京:清华大学出版社、南宁:广西科学技术大学出版社,1999.10
[62] 周志华、曹存根. 神经网络及其应用[M]. 北京:清华大学出版社,2004.9
[63] 何玉彬、李新忠著. 神经网络控制技术及其应用[M]. 北京:科学出版社,2000.11
[64] 飞思科技产品研发中心编著. MATLAB6.5 辅助神经网络分析与设计[M]. 北京:电子工业出版社,2003.01
[65] 张斌、严骏、毛宁. BP 神经网络在挖掘机电气系统故障诊断中的应用[J]. 土石方机械与施工技术,2005,No.9,pp.49-50