Automated Detection of Urban Road Manhole Covers Using Mobile Laser Scanning Data

This paper proposes a novel framework for automated detection of urban road manhole covers using mobile laser scanning (MLS) data. First, to narrow searching regions and reduce the computational complexity, road surface points are segmented from a raw point cloud via a curb-based road surface segmentation approach and rasterized into a georeferenced intensity image through inverse distance weighted interpolation. Then, a supervised deep learning model is developed to construct a multilayer feature generation model for depicting high-order features of local image patches. Next, a random forest model is trained to learn mappings from high-order patch features to the probabilities of the existence of urban road manhole covers centered at specific locations. Finally, urban road manhole covers are detected from georeferenced intensity images based on the multilayer feature generation model and random forest model. Quantitative evaluations show that the proposed algorithm achieves an average completeness, correctness, quality, and F1-measure of 0.955, 0.959, 0.917, and 0.957, respectively, in detecting urban road manhole covers from georeferenced intensity images. Comparative studies demonstrate the advantageous performance of the proposed algorithm over other existing methods for rapid and automated detection of urban road manhole covers using MLS data.

[1]  Shifeng Chen,et al.  Salient Object Detection via Random Forest , 2014, IEEE Signal Processing Letters.

[2]  Gustavo Carneiro,et al.  Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Joshua B. Tenenbaum,et al.  Learning with Hierarchical-Deep Models , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Alberto Broggi,et al.  Extensive Tests of Autonomous Driving Technologies , 2013, IEEE Transactions on Intelligent Transportation Systems.

[5]  Jun Yu,et al.  Automated Detection of Road Manhole and Sewer Well Covers From Mobile LiDAR Point Clouds , 2014, IEEE Geoscience and Remote Sensing Letters.

[6]  Nanning Zheng,et al.  Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms , 2007, IEEE Transactions on Intelligent Transportation Systems.

[7]  Nanning Zheng,et al.  Toward intelligent driver-assistance and safety warning system , 2004, IEEE Intelligent Systems.

[8]  Catherine Deegan,et al.  Mobile mapping system for the automated detection and analysis of road delineation , 2011 .

[9]  Masashi Morimoto,et al.  Circular object detection based on separability and uniformity of feature distributions using Bhattacharyya Coefficient , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[10]  Kelly Elder,et al.  Automatic Grain Type Classification of Snow Micro Penetrometer Signals With Random Forests , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Wang Yunhong.  Improved classical Hough transform applied to the manhole cover's detection and location , 2006 .

[12]  Norbert Pfeifer,et al.  A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Luc Van Gool,et al.  Hough Forests for Object Detection, Tracking, and Action Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Pengfei Liu,et al.  Automated extraction of manhole covers using mobile LiDAR data , 2014 .

[15]  D. Mahapatra,et al.  Analyzing Training Information From Random Forests for Improved Image Segmentation , 2014, IEEE Transactions on Image Processing.

[16]  David B. Dunson,et al.  Deep Learning with Hierarchical Convolutional Factor Analysis , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Naoki Tanaka,et al.  A Detection Method of Cracks and Structural Objects of the Road Surface Image , 2000, MVA.

[18]  Bingbing Ni,et al.  High-Order Local Spatial Context Modeling by Spatialized Random Forest , 2013, IEEE Transactions on Image Processing.

[19]  Geoffrey E. Hinton,et al.  An Efficient Learning Procedure for Deep Boltzmann Machines , 2012, Neural Computation.

[20]  Cheng Wang,et al.  Using mobile laser scanning data for automated extraction of road markings , 2014 .

[21]  Karl Rohr,et al.  Model-Based Detection and Localization of Circular Landmarks in Aerial Images , 2004, International Journal of Computer Vision.

[22]  Catherine Deegan,et al.  Assessment of stereo camera calibration techniques for a portable mobile mapping system , 2013, IET Comput. Vis..

[23]  Tijmen Tieleman,et al.  Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.

[24]  Dongwook Kim,et al.  Environment-Detection-and-Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment , 2012, IEEE Transactions on Intelligent Transportation Systems.

[25]  Craig L. Glennie,et al.  Synthesis of Transportation Applications of Mobile LIDAR , 2013, Remote. Sens..

[26]  Yun Shi,et al.  MANHOLE COVER DETECTION USING VEHICLE-BASED MULTI-SENSOR DATA , 2012 .

[27]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[28]  Luc Van Gool,et al.  Multi-view manhole detection, recognition, and 3D localisation , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).