A Hessian-based methodology for automatic surface crack detection and classification from pavement images

Abstract

Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

Crack Junction Detection in Pavement Image Using Correlation Structure Analysis and Iterative Tensor Voting

“…Some methods of crack detection and classification are based on crack topology and a priori knowledge [10]- [12]. Ghanta et al adopted the Hessian matrix to extract crack direction and classified pavement cracks into three types based on the direction and the area covered [13]. Arena et al focused on crack quantification by crack topology, such as the orientation, length, width and aspect ratio of the cracks, which were important to analyze the type and severity of cracks [14].…”

An Automatic Road Distress Visual Inspection System Using an Onboard In-Car Camera

“…Although nondestructive vision-based road analysis systems are not new in research communities, many problems and challenges have remained unsolved and researchers from many countries have continued proposing new solutions as some recent examples from United States [1,2], Canada [3], France [4], and Italy [5]. Nevertheless, in the context of roads in Bangkok, the capital city of Thailand, there are some unique scenarios that have never been addressed by previous works but they do affect the ease of use and practicality of an automatic road analysis system in Bangkok, for instance, inconsistent road conditions, damaged road markings, confusing driving styles, and traffic problems.…”