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3D Crack Measurement for High-Dimensional Geometry
Authors:Nathan Black, Ork Fåk Olson and Mike Lovell (Pavemetrics)
Abstract:The LCMS system provides unique capabilities to quantify and detect cracks and indentations in road surfaces. This paper focuses on the data acquisition and related 3D process of the LCMS system. The first step in the process of 3D crack and indentation data acquisition involves running the LCMS along each of the designated routes of interest. This process includes both identifying crack and indentation features and collecting the relevant LCMS data. Additionally, the LCMS system is capable of acquiring a unique combination of geometrical data and feature location information as part of the crack and indentation measurement process. The data can be subsequently used to build, store, and visualize 3D road surfaces using various visualization tools. 3D modeling tools are also used to perform various geometrical analysis on the road surfaces such as calculating geometric parameters and verifying the validity of the data collected by the LCMS. The data is also used by Pavemetrics for various applications such as road network mapping and performing real-time simulation of pavement conditions.
In the first part of this report, we will discuss a method to automatically detect sealed cracks in images. We present the approach developed by Pavemetrics in conjunction with their partner at INO (National Optics Institute) in Canada. Three different methods are presented and evaluated for the detection and classification of sealed cracks. First, we describe a unsupervised approach which extracts an image of a sealed crack by thresholding the RGB (Red Green Blue) image of the road surface. Second, we describe an unsupervised approach based on SIFT (Scale invariant feature transform) in combination with edge information. The third method is a supervised approach based on SIFT also combined with a gradient vector flow (GVF). Then, we illustrate the comparison of the performance of these unsupervised and supervised approaches. We will evaluate these techniques on two different road surfaces: DGA and chipseal. For each of these surfaces, we will also analyze the potential bias that is introduced into the cracks parameters (crack depth, width and crack length) when the main vehicle tire is present on the lane when processing images.
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