Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Baoxian Li,Allen Zhang,Yue Fei,Kelvin C P Wang,Giuseppe Sollazzo

doi:10.1155/2019/1813763

Abstract

Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.

Highlights

Effective and efficient pavement condition assessment is crucial for determining pavement maintenance schedules, evaluating performance, planning rehabilitation, etc
Because pavement cracking is an important indicator of pavement deterioration and deficiency, it is widely considered as an integral part of regional pavement distress surveys [1]
With the emergence of advanced technologies such as high-speed and high-resolution 3D industry cameras, the pavement inspection methods based on 3D scanning have attracted more and more interests for the following reasons: (1) The surface information in 3D images collected by advanced data acquisition systems is more accurate than those in 2D images

Summary

Introduction

Effective and efficient pavement condition assessment is crucial for determining pavement maintenance schedules, evaluating performance, planning rehabilitation, etc. Edge detection based methods, such as morphological filters [21] and BEMD [22], are introduced for pavement crack detection. An image library of 200 pavement 3D data verifies the accuracy and effectiveness of the proposed method. All the testing and validation data are 3D pavement images collected by PaveVision3D System.

Results

Conclusion