基于数字图像处理的货车超限监测算法研究
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摘要
随着铁路安全技术装备的完善，现在铁路运营安全的主要威胁来自因装载、冲撞、制动、震动等因素引起的货车所载货物超出铁路货运装载限界。目前，我国铁路货运管理部门主要采用人工测量方法来检测货车超限情况。但是人工测量只能实现对静止货车装载情况的检测，并且检测效率低，检测精度也难以保障。为了确保铁路货运的运营安全，实现对运行中货车的装载情况的自动监测是必要的。
本文以数字图像处理原理中图像分割理论为基础，结合货车装载状态监测的实际情况和货车装载状态监测系统对图像分割的要求，以保证超限漏检率最小为目标，给出了货车图像自动分割的算法，获得货车及其所装载货物的分布区域，再根据该分布区域，得出货车装载状态是否超限的结论，从而实现对运行中货车的装载情况的自动监测。采集货车运行图像对算法进行测试，测试结果表明，该算法适应性较好，检测精度和速度基本能满足要求。
The threats of the security of raiIway freight mostly come from cargoes exceeding over the railway gauge which caused by loading, collision, braking and shaking etc. Today, the loading situation is most Iy checked manua My, wh i ch can on Iy check the Ioad i ng s i tuat i on of the static freight train with the low efficiency and precision. The automat i c mon i tor i ng on the I oad i ng s i tuat i on of mov i ng f re i ght tra i ns should be implemented to ensure the operation security of railway fre i ght.
The paper presents a new algorithm for freight train image segmentation with the aim of minimizing missing gauge-exceeding detection rate based on the image segmentation theory. The prof i le of the freight train and cargoes on the image is obtained with the arithmetic and whether cargoes exceeding the gauge or not is concluded by the location of the freight train and cargoes on the image. The algorithm is verified by images of moving freight train. It is proved that the algorithm has good adaptability, acceptable precision and satisfied processing speed in normal climate.
本文以数字图像处理原理中图像分割理论为基础，结合货车装载状态监测的实际情况和货车装载状态监测系统对图像分割的要求，以保证超限漏检率最小为目标，给出了货车图像自动分割的算法，获得货车及其所装载货物的分布区域，再根据该分布区域，得出货车装载状态是否超限的结论，从而实现对运行中货车的装载情况的自动监测。采集货车运行图像对算法进行测试，测试结果表明，该算法适应性较好，检测精度和速度基本能满足要求。
The threats of the security of raiIway freight mostly come from cargoes exceeding over the railway gauge which caused by loading, collision, braking and shaking etc. Today, the loading situation is most Iy checked manua My, wh i ch can on Iy check the Ioad i ng s i tuat i on of the static freight train with the low efficiency and precision. The automat i c mon i tor i ng on the I oad i ng s i tuat i on of mov i ng f re i ght tra i ns should be implemented to ensure the operation security of railway fre i ght.
The paper presents a new algorithm for freight train image segmentation with the aim of minimizing missing gauge-exceeding detection rate based on the image segmentation theory. The prof i le of the freight train and cargoes on the image is obtained with the arithmetic and whether cargoes exceeding the gauge or not is concluded by the location of the freight train and cargoes on the image. The algorithm is verified by images of moving freight train. It is proved that the algorithm has good adaptability, acceptable precision and satisfied processing speed in normal climate.
引文
[1] T Arch, A Kaup. Statistical model-based change detection in moving video[J]. Signal Processing, 1993,31(3):165-180
[2] T Arch, A Kaup, R Mester. Change detection in image sequences using Gibbs random fields: A Bayesian approach[A]. in: Proceedings of International Workshop on Intelligent Signal Processing and Communication Systems, Sendai, Japan, 1993.56-61
[3] Philippe Salembier. Morphological multiscale segmentation for image coding[J]. Signal Processing, 1994;38:359-386
[4] Munchurl Kim, Jae Gark choi et al. A VOP generartion tool: Automatic Segmentatic of Moving Objects in Image Sequences Based on Spatio-Temporal Information[J]. IEEE Trans on Circuits and Systems for Video Technology. 1992.12;9(8):1216-1226
[5] L Vincent, P Soille. Watershed in digital space: an and implementation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 1991.6;13(6):582-598
[6] S Beucher, F Meyer. The morphological approach to segmentation: the watershed transformation[J]. Mathematical morphology in Image Processing. 1993:433-481
[7] R Mech, M Wollborn. A noise robust method for segmentation of moving objects in video sequences[J]. Signal Processing, 1998,66(2)
[8] Neri A, Colonnese S. Automatic moving object and background separation[J]. Signal Processing, 1998;66(2);203-217
[9] Wang. The layer representation of the image[J]. IEEE Trans. on Image Processing, 1994.9;3:625-638
[10] Demin Wang. Unsupervised video segmentation based on watersheds and temporal tracking[J]. IEEE Trans on Circuits and video technology. 1998.9;8(4):339-546
[11] Kompatsiaris, Ioannis. 3D Model-Based Segmentation of Videoconference Image equences[J]. IEEE Trans on Circuits and Systems for Video
Technology, 1998;8(5):47-560
[12] Jian John Lu, Michael J Rechtorik, Shiyu Yang. Automatic Vehicle Identification Technology Applications to Toll Collection Services. Transportation Research Record 1588,971136:18-25
[13] R. M. Haralick and L. G. Shapino. Survey: Image Segmentation. Comput. Vision, Graphics, Image Proc. 29:100-132,1985.
[14] A. Rosenfield. Connectiveity in Digital Pictures. Journal of the ACM, 17: 146-160, 1970.
[15] T Meier, K N Ngan. Automatic segmentation of moving for video object plane generation[J]. IEEE Transactions on Circuits an Systems for Video Technology, 1998,8(5):525-538
[16] Rolf Adams, Leanne Bischof. Seed Region Growing. IEEE Trans. on PAMI, 1994.6:640-647
[17] T Meier, K N Ngan. Automatic segmentation of moving for video object plane generation[J]. Signal Processing, 1993, 31(3): 165-180
[18] ISO/IECJTC1/SC29/WG11 M3449-1998, Refined procedure for objective evaluation of video object generation algorithms[S]
[19] Cunllia Borgefors. Analyzing Nonconvex 2D and 3D Patterns. CVIU, 1996.1:145-157
[20] S Lhorowitz, T Pavlids. Picture segmentation by a tree traversal algorithm. J. Assoc. Comput., Mach. 23, 1976:368-388
[21] Rolf Adams, Leanne Bischof. Seed Region Growing. IEEE Trans. on PAMI, 1994.6:640-647
[22] Shiuh-yung Chen, Wei-Chung Lin, Chin-tu Chen. Split-and-Merge Image Segmentation Based on Localized Feature Analysis and Statistical Tests. CVGIP: GMIP, 1991.9; 53:457-475
[23] 卡斯尔曼(Castleman，K．R．)．图形图像处理．朱志刚、林学訚、石定机等译．电子工业出版社．2002．2
[24] 傅德胜，寿亦禾．图形图像处理学．东南大学出版社．2001．12
[25] 程桂明等．应用MATLAB语言处理数字信号与数字图像．科学出版社．2000．1
[26] 章毓晋．图像处理和分析．清华大学出版社．1999．3
[27] 孙即祥等．模式识别中的特征提取与计算机视觉不变量．国防工业出
版社．2001．9
[28] 章毓晋．过渡区和图像分割．电子学报．1996a，24(1)：12-17
[29] 章毓晋．图像分割评价技术分类和比较．中国图像图形学报．1996c，1(2)：170-174
[30] 苏光大．微机图像处理系统．清华大学出版社，2000．7
[31] 吴立德．计算机视觉．复旦大学出版社．1993
[32] 荆仁杰等．计算机图像处理．河北教育出版社．1990
[33] 程民德等．图像识别导论．上海科学技术出版社．1983
[34] 陈果，佐洪福．图像分割的二维最大熵遗传算法．计算机辅助设计与图形学学报．2002，14(6)：530-534
[35] 张毅军等．二维熵图像阈值分割的快速递推算法[J]．模糊识别与人工智能．1997，10(3)：259-264
[36] 李立源．基于二维灰度直方图最佳—维投影的图像分割方法[J]．自动化学报．1996，22(3)：315-322
[37] 龚坚．基于二维灰度直方图Fisher线性分割的图像分割方法[J]．模式识别与人工智能．1997，10(1)：1-7
[38] 夏良正．数字图像处理[M]．东南大学出版社．1999：223-228
[39] 王润生．图像理解．国防科技大学出版社．1995
[40] 蔡元龙．模式识别．西安电子科技大学出版社．1990
[41] 蔡涛，王润生．组合利用灰度和几何特性的图像分割算法．计算机工程与应用．2001．5：62-64
[42] 邬正平等，一种基于动态规划的视频分割方法．计算机辅助设计与图形学学报，2002，14(8)：743-746
[43] 任海兵等．连续动态手势的时空表观建模及识别[J]．计算机学报．2000，23(8)：824-828
[44] 孙怡等．人体腿部运动图像的跟踪[J]．模式识别与人工智能．2001，14(1)：82-85
[45] 朱菊华等．核磁共振图像的预处理．计算机工程．2001，27(2)：25-26
[46] 陈果等．图像分割的二维最大熵遗传算法．计算机辅助设计与图形学学报．2002，14(6)：530-534
[47] 章毓晋．图像分割[M]．科学出版社．2001
[48] 葛云等．基于Legendre正交矩的配准方法及其在二值图像配准中的应用[J]．电子学报．2001，29(1)：54-56
[49] 章毓晋等．利用切线方向信息检测亚像素边缘[J]．模式识别与人工智能．1997，10(1)：83-88
[50] 姚玉荣等．利用小波和矩进行基于形状的图像检索[J]．中国图像图形学报．2000，5A(3)：206-210
[51] 陆海斌等．一种高效的视频切变检测算法[J]．中国图像图形学报．1999，4(10)：805-810
[52] 王惠锋等．基于内容的图像检索中的语义处理方法[J]．中国图像图形学报．2001，6A(10)：945-952
[53] 李殿柱．铁路货物运输．西南交大出版社．1992．8
[2] T Arch, A Kaup, R Mester. Change detection in image sequences using Gibbs random fields: A Bayesian approach[A]. in: Proceedings of International Workshop on Intelligent Signal Processing and Communication Systems, Sendai, Japan, 1993.56-61
[3] Philippe Salembier. Morphological multiscale segmentation for image coding[J]. Signal Processing, 1994;38:359-386
[4] Munchurl Kim, Jae Gark choi et al. A VOP generartion tool: Automatic Segmentatic of Moving Objects in Image Sequences Based on Spatio-Temporal Information[J]. IEEE Trans on Circuits and Systems for Video Technology. 1992.12;9(8):1216-1226
[5] L Vincent, P Soille. Watershed in digital space: an and implementation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 1991.6;13(6):582-598
[6] S Beucher, F Meyer. The morphological approach to segmentation: the watershed transformation[J]. Mathematical morphology in Image Processing. 1993:433-481
[7] R Mech, M Wollborn. A noise robust method for segmentation of moving objects in video sequences[J]. Signal Processing, 1998,66(2)
[8] Neri A, Colonnese S. Automatic moving object and background separation[J]. Signal Processing, 1998;66(2);203-217
[9] Wang. The layer representation of the image[J]. IEEE Trans. on Image Processing, 1994.9;3:625-638
[10] Demin Wang. Unsupervised video segmentation based on watersheds and temporal tracking[J]. IEEE Trans on Circuits and video technology. 1998.9;8(4):339-546
[11] Kompatsiaris, Ioannis. 3D Model-Based Segmentation of Videoconference Image equences[J]. IEEE Trans on Circuits and Systems for Video
Technology, 1998;8(5):47-560
[12] Jian John Lu, Michael J Rechtorik, Shiyu Yang. Automatic Vehicle Identification Technology Applications to Toll Collection Services. Transportation Research Record 1588,971136:18-25
[13] R. M. Haralick and L. G. Shapino. Survey: Image Segmentation. Comput. Vision, Graphics, Image Proc. 29:100-132,1985.
[14] A. Rosenfield. Connectiveity in Digital Pictures. Journal of the ACM, 17: 146-160, 1970.
[15] T Meier, K N Ngan. Automatic segmentation of moving for video object plane generation[J]. IEEE Transactions on Circuits an Systems for Video Technology, 1998,8(5):525-538
[16] Rolf Adams, Leanne Bischof. Seed Region Growing. IEEE Trans. on PAMI, 1994.6:640-647
[17] T Meier, K N Ngan. Automatic segmentation of moving for video object plane generation[J]. Signal Processing, 1993, 31(3): 165-180
[18] ISO/IECJTC1/SC29/WG11 M3449-1998, Refined procedure for objective evaluation of video object generation algorithms[S]
[19] Cunllia Borgefors. Analyzing Nonconvex 2D and 3D Patterns. CVIU, 1996.1:145-157
[20] S Lhorowitz, T Pavlids. Picture segmentation by a tree traversal algorithm. J. Assoc. Comput., Mach. 23, 1976:368-388
[21] Rolf Adams, Leanne Bischof. Seed Region Growing. IEEE Trans. on PAMI, 1994.6:640-647
[22] Shiuh-yung Chen, Wei-Chung Lin, Chin-tu Chen. Split-and-Merge Image Segmentation Based on Localized Feature Analysis and Statistical Tests. CVGIP: GMIP, 1991.9; 53:457-475
[23] 卡斯尔曼(Castleman，K．R．)．图形图像处理．朱志刚、林学訚、石定机等译．电子工业出版社．2002．2
[24] 傅德胜，寿亦禾．图形图像处理学．东南大学出版社．2001．12
[25] 程桂明等．应用MATLAB语言处理数字信号与数字图像．科学出版社．2000．1
[26] 章毓晋．图像处理和分析．清华大学出版社．1999．3
[27] 孙即祥等．模式识别中的特征提取与计算机视觉不变量．国防工业出
版社．2001．9
[28] 章毓晋．过渡区和图像分割．电子学报．1996a，24(1)：12-17
[29] 章毓晋．图像分割评价技术分类和比较．中国图像图形学报．1996c，1(2)：170-174
[30] 苏光大．微机图像处理系统．清华大学出版社，2000．7
[31] 吴立德．计算机视觉．复旦大学出版社．1993
[32] 荆仁杰等．计算机图像处理．河北教育出版社．1990
[33] 程民德等．图像识别导论．上海科学技术出版社．1983
[34] 陈果，佐洪福．图像分割的二维最大熵遗传算法．计算机辅助设计与图形学学报．2002，14(6)：530-534
[35] 张毅军等．二维熵图像阈值分割的快速递推算法[J]．模糊识别与人工智能．1997，10(3)：259-264
[36] 李立源．基于二维灰度直方图最佳—维投影的图像分割方法[J]．自动化学报．1996，22(3)：315-322
[37] 龚坚．基于二维灰度直方图Fisher线性分割的图像分割方法[J]．模式识别与人工智能．1997，10(1)：1-7
[38] 夏良正．数字图像处理[M]．东南大学出版社．1999：223-228
[39] 王润生．图像理解．国防科技大学出版社．1995
[40] 蔡元龙．模式识别．西安电子科技大学出版社．1990
[41] 蔡涛，王润生．组合利用灰度和几何特性的图像分割算法．计算机工程与应用．2001．5：62-64
[42] 邬正平等，一种基于动态规划的视频分割方法．计算机辅助设计与图形学学报，2002，14(8)：743-746
[43] 任海兵等．连续动态手势的时空表观建模及识别[J]．计算机学报．2000，23(8)：824-828
[44] 孙怡等．人体腿部运动图像的跟踪[J]．模式识别与人工智能．2001，14(1)：82-85
[45] 朱菊华等．核磁共振图像的预处理．计算机工程．2001，27(2)：25-26
[46] 陈果等．图像分割的二维最大熵遗传算法．计算机辅助设计与图形学学报．2002，14(6)：530-534
[47] 章毓晋．图像分割[M]．科学出版社．2001
[48] 葛云等．基于Legendre正交矩的配准方法及其在二值图像配准中的应用[J]．电子学报．2001，29(1)：54-56
[49] 章毓晋等．利用切线方向信息检测亚像素边缘[J]．模式识别与人工智能．1997，10(1)：83-88
[50] 姚玉荣等．利用小波和矩进行基于形状的图像检索[J]．中国图像图形学报．2000，5A(3)：206-210
[51] 陆海斌等．一种高效的视频切变检测算法[J]．中国图像图形学报．1999，4(10)：805-810
[52] 王惠锋等．基于内容的图像检索中的语义处理方法[J]．中国图像图形学报．2001，6A(10)：945-952
[53] 李殿柱．铁路货物运输．西南交大出版社．1992．8