Abstract
Abstract. With an ever-increasing network of thousands of miles of pavement laid out over highways, road networks, and airport runways, their continuous monitoring is a task of utmost importance to public agencies responsible for their maintenance. The existing approaches mostly rely on a manual detection of pavement distress based on acquired image or video data – an approach that is time-consuming, costly, and whose results are subjective to the designated rater. This necessitates the need for a system that is capable of a quick data acquisition along with an efficient algorithm for the detection and quantification of pavement distress based on the acquired data. This paper proposes a LiDAR-based pavement distress detection and quantification using a mobile mapping system (MMS). Starting with a comparison of a medium-grade and high-grade MMS in terms of their accuracy and captured level of detail, this paper proves the ability of the high-grade MMS to allow the detection of shallow potholes and cracks in the pavement. Next, a fully automated algorithm is proposed to detect pavement distress from 3D point cloud followed by a quantification of the severity (in terms of the depth and volume) of the detected potholes/cracks. Finally, an experimental verification conducted over a 10 mile highway segment and two airport runway strips indicates the efficient performance of the proposed data acquisition system as well as the algorithm to report the pavement distress ranging from shallow cracks over airport runways to deeper potholes along highway segments.
Highlights
Highways, road networks, and airport runways cumulatively comprise thousands of miles of pavement laid out using asphalt, concrete, or composite materials
Three samples of detected potholes over a highway road segment are shown in Figure 5, where Figure 5 (a) shows a pothole that is about 10 cm deep occurring due to a longitudinal wear-and-tear of the pavement, Figure 5 (b) depicts a 4 cm deep pothole as a result of pavement patching discontinuity, and Figure 5 (c) illustrates a 2 cm deep depression caused by a missing Raised Pavement Marker (RPM)
Detected potholes over highway road segment: (a) 10 cm deep pothole caused by pavement wear-and-tear, (b) 4 cm deep pothole caused by missing RPM, and (c) 2 cm deep pothole caused by pavement patching discontinuity
Summary
Road networks, and airport runways cumulatively comprise thousands of miles of pavement laid out using asphalt, concrete, or composite materials. The acquired image and video data over pavements are manually inspected by technicians on computer monitors to detect any defects. Besides this being a time-consuming and costly task, the final results are influenced by the subjectivity and the experience of the raters (Bianchini, 2010). There are four major choices for sensor modalities – vibration-based, vision-based, thermography-based, and LiDARbased – to acquire data for pavement inspection. Each of these methods of data acquisition have been coupled with different algorithms for varying aspects of pavement surface assessment. In the area of vibration-based methods, Yu and Yu (2006)
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