Degenerate Near-Planar 3D Reconstruction from Two Overlapped Images for Road Defects Detection

Yazhe Hu,Tomonari Furukawa

doi:10.3390/s20061640

Abstract

This paper presents a technique to reconstruct a three-dimensional (3D) road surface from two overlapped images for road defects detection using a downward-facing camera. Since some road defects, such as potholes, are characterized by 3D geometry, the proposed technique reconstructs road surfaces from the overlapped images prior to defect detection. The uniqueness of the proposed technique lies in the use of near-planar characteristics of road surfaces‘ in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem. The reconstructed road surfaces thus result from the richer information. Therefore, the proposed technique detects road surface defects based on the accuracy-enhanced 3D reconstruction. Parametric studies were first performed in a simulated environment to analyze the 3D reconstruction error affected by different variables and show that the reconstruction errors caused by the camera’s image noise, orientation, and vertical movement are so small that they do not affect the road defects detection. Detailed accuracy analysis then shows that the mean and standard deviation of the errors are less than mm and 1 mm through real road surface images. Finally, on-road tests demonstrate the effectiveness of the proposed technique in identifying road defects while having over 94% in precision, accuracy, and recall rate.

Highlights

A road is one of the most fundamental infrastructures in the transportation system
Past works on automatic road defects detection can be classified into three types: the acceleration-based detection, the color-based detection, and the geometry-based detection
The simulation experiments analyzed the influence of different variables to the proposed road surface reconstruction

Summary

Introduction

The road surface condition inevitably downgrades and is affected by stresses from traffic as well as climate impacts such as humidity or temperature change. Inefficiency, and subjectivity of manual inspection have resultantly necessitated automatic measurement of the road surface defects such as potholes and ruts, which are mostly characterized by geometry [3,4,5,6,7,8]. Past works on automatic road defects detection can be classified into three types: the acceleration-based detection, the color-based detection, and the geometry-based detection. Yu et al [9] analyzed acceleration and automatically detected road defects for the first time to the best of the authors’ knowledge. Vittorio et al [10] detected the road anomalies based on the abnormal accelerometer data from the cellphone. Tai et al [11] and Eriksson et al [12] proposed a technique using a machine learning approach to detect road anomaly where Support Vector Machine

Methods

Results

Conclusion