Automatic detection of pavement distress in road freight transport risks

Intelligent and automatic detection of pavement distress is a necessary mean to guarantee the safety and the comfort of road freight vehicles, which plays an important role on the pavement maintenance and the freight transportation. Based on analyzing neighboring gray difference, local minimum gray analysis and the sub-block label, a joint automatic detection method of the pavement distress is proposed in this paper. Theoretical analysis and experimental results show that the proposed joint detection method of pavement distress is more effective, and the detection rate of pavement images is 96.7%.

[1]  Peng Gong,et al.  Integrated shadow removal based on photogrammetry and image analysis , 2005 .

[2]  Touradj Ebrahimi,et al.  Cast shadow segmentation using invariant color features , 2004, Comput. Vis. Image Underst..

[3]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[4]  Chen Zhang.  An Edge Detection Method Designed for Pavement Distress Images , 2011 .

[5]  Tai-Pang Wu,et al.  A Bayesian approach for shadow extraction from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  Paulo Lobato Correia,et al.  Automatic Road Crack Detection and Characterization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[7]  Emanuele Frontoni,et al.  Road pavement crack automatic detection by MMS images , 2013, 21st Mediterranean Conference on Control and Automation.

[8]  Bo Shaobo,et al.  Pavement Crack Detection and Analysis for High-grade Highway , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[9]  Song Hong-xun.  Pavement Distress Detection Algorithm Based on Tiles in Wavelet Domain , 2009 .