https://nottingham-repository.worktribe.com/preview/745918/Manuscript_KochEtAl_2015_accepted.pdf

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Pavement condition information is a significant component in Pavement Management Systems. The labeling and quantification of the type, severity, and extent of surface cracking is a challenging area for weighing the asphalt pavements. This paper presents a widespread review on various platform and image processing approaches for asphalt surface interpretation. The main part of this study presents a comprehensive combination of the state of the art in image processing based on crack interpretation related to asphalt pavements. An attempt is made to study the existing methodologies from different points of views accompanied by extensive comparisons on three stages of methods—distress detection, classification, and quantification to facilitate further research studies. This paper presents a survey of the developed pavement inspection systems up to date. Additionally, emerging and evolution technologies considered to automate the processes are discussed.

Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review

Evaluating the condition of transportation infrastructure is an expensive, labor intensive, and time consuming process. Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations. The measurement of damage and deterioration is often qualitative and limited to point observations. Remote sensing techniques offer nondestructive methods for road condition assessment with large spatial coverage. These tools provide an opportunity for frequent, comprehensive, and quantitative surveys of transportation infrastructure. The goal of this paper is to provide a bridge between traditional procedures for road evaluation and remote sensing methodologies by creating a comprehensive reference for geotechnical engineers and remote sensing experts alike. A comprehensive literature review and survey of current techniques and research methods is provided to facilitate this bridge. A special emphasis is given to the challenges associated with transportation assessment in the aftermath of major disasters. The use of remote sensing techniques offers new potential for pavement managers to assess large areas, often in little time. Although remote sensing techniques can never entirely replace traditional geotechnical methods, they do provide an opportunity to reduce the number or size of areas requiring site visits or manual methods.

/pdf/review-of-remote-sensing-methodologies-for-pavement-wiovcknndd.pdf

Review of remote sensing methodologies for pavement management and assessment

Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on two-dimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.

/pdf/image-based-pothole-detection-system-for-its-service-and-4kkf4aw6uf.pdf

Image-Based Pothole Detection System for ITS Service and Road Management System

In recent years, extensive research has been conducted on pavement distress detection. A large part of these studies applied automated methods to capture different distresses. In this paper, a literature review on the distresses and related detection methods are presented. This review also includes commercial solutions. Thereafter, a gap analysis is conducted which is concluded that in particular the distresses related to pavement micro-texture need serious additional research in order to be implemented in a cost-effective fashion. Depth-related distresses are detectible fairly well, but rely on expensive tools.

/pdf/a-review-on-automated-pavement-distress-detection-methods-304gj2fw0f.pdf

A review on automated pavement distress detection methods

Method for Automated Assessment of Potholes, Cracks and Patches from Road Surface Video Clips

Automated processing of road surface video clips captured for road condition assessment is necessary to detect the existence of road surface distress in less time and efforts. This paper presents a robust method for automated segmentation of frames with/without distress from road surface video clips captured by existing camera based imaging systems without any artificial lighting systems. The proposed method is based on an adaptive thresholding technique and user defined decision logic for automated detection of road surface distresses out of such video clips. This method has been implemented in a Windows Vista environment with the help of Visual Studio 2008 and OpenCV library and tested on 31 road surface video clips of Indian Highways. The results show that this method can segment a road surface video clip automatically into two categories of video frames, namely frames with distress and frames without distress, with accuracy up to 96% while saving a considerable amount of time and manpower resources.

Robust Method for Automated Segmentation of Frames with/without Distress from Road Surface Video Clips

The assessment of cracks is an essential part of a road management system for developing repair and maintenance strategies. Nowadays, road condition assessment is gradually being automated by using various imaging systems. However, the processing of the collected road visual data is still being done manually or semi-automatically in India. There are thousands of kilometres (50,000 km aprox.) of raw video data of Indian National Highways in Central Road Research Institute collected using road network survey vehicles under the Ministry of Road Transport & Highways (MORTH) sponsored research & development (R&D) Scheme (T-5) "Development of Geographic Information System (GIS) based National Highways Information System." Existing methods of data processing are very costly and time consuming. In this paper, a method for automated detection and measurement of cracks out of a large volume of monochrome video clips of Indian Highways is presented. In the proposed method, all the frames of a raw video clip are extracted and grouped automatically using an algorithm into two different categories, viz. clear frames, i.e., without distress, and suspicious frames which may have distress. Then, frames belonging to the suspicious category are subjected to various image processing techniques supported by user defined decision logic for the cracks detection and their measurement. This methodology has been implemented in a Windows XP environment with the help of Visual Studio 2008 and OpenCV, an open source computer vision library, and tested on five different road video clips. The results show that this method can detect and measure cracks automatically with accuracy up to 93%, while saving considerable time and manpower resources. The information extracted using the proposed method can be used for determining maintenance levels of Indian roads and taking further appropriate actions for repair and maintenance.

Automated detection and measurement of cracks from monochrome video clips captured by road network survey vehicle

The assessment of potholes is an essential part of a road maintenance system for developing repair and maintenance strategies. Nowadays, road condition assessment is gradually being automated by using various imaging systems. However, the processing of the collected raw visual data is still being done manually or semi-automatically in India. Existing methods of data processing are very costly, time consuming and mostly focused on the detection and classification of cracks only. This paper presents an efficient and accurate algorithm for automated detection and measurement of potholes out of a large volume of road surface video clips of Indian Highways. In the proposed algorithm, potholes are detected and their features are measured automatically out of a video clip in one go, using various image processing techniques supported by user defined decision logic. This algorithm is implemented in a Windows environment with the help of Visual Studio 2008 and OpenCV, an open source computer vision library, and tested on nine different road video clips. The results show that this algorithm can detect and measure potholes automatically with accuracy up to 94%. The information extracted using the proposed algorithm can be used for determining maintenance levels of Indian roads and taking further appropriate actions for repair and maintenance.

Lalit K Das

Papers

Method for Automated Assessment of Potholes, Cracks and Patches from Road Surface Video Clips

Robust Method for Automated Segmentation of Frames with/without Distress from Road Surface Video Clips

Automated detection and measurement of cracks from monochrome video clips captured by road network survey vehicle

Automated detection and measurement of potholes from road surface video clips