Development of a Multi-Sensor System for Road Condition Mapping

Abstract. We present a concept for a vehicle based road condition mapping system using infrared spectrometers, high resolution RGB cameras and a laser scanner. Infrared spectrometry is employed to monitor the deterioration of the surface material and pavement condition, in particular by aging. High resolution RGB imaging enables automatic asphalt crack detection and provides base images for spectrometry spots. Laser scanning aims at the detection of geometrical road irregularities and pavement failures such as potholes and ruts. These three major recordings contribute to the analysis of the pavements condition. All mapping sensors are synchronised with a navigation sensor to collect geo-referenced data. The concept of road condition mapping relies on a separate analysis of the different sensor data which are related to road sections. Processing results like the percentage of the road section area related to cracks, pot holes, ruts etc. are merged to achieve an assessment for the road section. The processes for assessing deterioration from the spectrometer data, the detection of ruts from the laser data and cracks from the images are discussed in detail and outlined with some experiments.

[1]  Tao Wang,et al.  Beamlet Transform Based Pavement Image Crack Detection , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[2]  J. Leese,et al.  An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross Correlation , 1971 .

[3]  K. Segl,et al.  Endmember detection in urban environments using hyperspectral HyMap data , 2003 .

[4]  Christoph Mertz.  Continuous Road Damage Detection Using Regular Service Vehicles , 2011 .

[5]  Harvey F. Silverman,et al.  A Class of Algorithms for Fast Digital Image Registration , 1972, IEEE Transactions on Computers.

[6]  Peggy Subirats,et al.  A combined wavelet-based image processing method for emergent crack detection on pavement surface images , 2004, 2004 12th European Signal Processing Conference.

[7]  Hermann Kaufmann,et al.  Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data , 2007 .

[8]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[9]  Tamás Lovas.  Photogrammetric pavement detection system , 2008 .

[10]  Margaret E. Gardner,et al.  Spectrometry and Hyperspectral Remote Sensing of Urban Road Infrastructure , 2003 .

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