A Multi-frame Stereo Vision-Based Road Profiling Technique for Distress Analysis

This paper presents a novel technique for road distress identification using an in-vehicle stereo vision system. The novelty of the proposed method is credited by the accumulation of multiple-frame 3D reconstructions which are properly aligned to a road-centred coordinate system. A 3D plane-fitting technique is first carried out to approximate the road manifold at the beginning of accumulation, followed by the construction of a digital elevation model (for the road being analysed) from multiple frames that are integrated using a stereo visual odometry algorithm. Potholes are detected as "valleys" from the built digital elevation model by means of connectedness analysis. Experimental results show significant improvements (> 30%) over a conventional method based on a single-frame construction of plane-based road models.

[1]  Naim Dahnoun,et al.  Real-time pothole detection on TMS320C6678 DSP , 2016, 2016 IEEE International Conference on Imaging Systems and Techniques (IST).

[2]  Umberto Spagnolini,et al.  Multitarget detection/tracking for monostatic ground penetrating radar: application to pavement profiling , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[4]  Jau-Woei Perng,et al.  Design and implementation of an intelligent road detection system with multisensor integration , 2016, 2016 International Conference on Machine Learning and Cybernetics (ICMLC).

[5]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[6]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[7]  Marcin STANIEK Neural Networks in Stereo Vision Evaluation of Road Pavement Condition , 2015 .

[8]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[9]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[10]  Xi Wang,et al.  High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth , 2014, GCPR.

[11]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[12]  Azriel Rosenfeld,et al.  Digital geometry , 2002, JCIS.

[13]  Jean-Philippe Tarel,et al.  Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[14]  Christian Koch,et al.  Pothole detection in asphalt pavement images , 2011, Adv. Eng. Informatics.

[15]  T. Vaudrey,et al.  Differences between stereo and motion behaviour on synthetic and real-world stereo sequences , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.

[16]  Naim Dahnoun,et al.  An efficient algorithm for pothole detection using stereo vision , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Tang Nan,et al.  Laser-based system for highway pavement texture measurement , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[18]  Reinhard Klette,et al.  Regularised Energy Model for Robust Monocular Ego-motion Estimation , 2017, VISIGRAPP.

[19]  Reinhard Klette,et al.  Road surface distress detection in disparity space , 2017, 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ).

[20]  Chih-Wei Yi,et al.  Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies , 2015, IEEE Transactions on Intelligent Transportation Systems.

[21]  Thegaran Naidoo,et al.  Visual surveying platform for the automated detection of road surface distresses , 2014, Other Conferences.

[22]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.

[23]  Sergiu Nedevschi,et al.  Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[24]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[25]  John Laurent,et al.  Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[26]  John G. Rarity,et al.  Obstacle detection using U-disparity on quadratic road surfaces , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[27]  Reinhard Klette,et al.  Towards Ubiquitous Autonomous Driving: The CCSAD Dataset , 2015, CAIP.

[28]  Reinhard Klette,et al.  Concise Computer Vision: An Introduction into Theory and Algorithms , 2014 .