Pothole detection in asphalt pavement images

Koch, Christian; Brilakis, Ioannis

doi:10.1016/j.aei.2011.01.002

Cited by 476 publications

(248 citation statements)

References 22 publications

(31 reference statements)

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“…Image processing methods using cameras are popular, speedy and inexpensive. Several image processing studies have been done in order to detect defects automatically [4,5,6,7,8]. However, the images are affected by the light conditions and their analysis requires supplementary information [9].…”

Section: Methods For Surface Defects Inspectionmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-According to Statistics Canada, bridges in Canada have a service life of approximately 43 years. With the majority of bridges passing half of their expected service life, a large amount of investment needs to be made to inspect and maintain them in a safe condition. Manual inspection methods are both time-consuming and costly, which may discourage further inspections and follow-up of defects. Thus, there is a great need for using an automated data collection system. Surface defects (e.g. cracks) in concrete bridges can be inspected using 3D Light Detection and Ranging (LiDAR) scanner as a Non-Destructive Testing (NDT) method. However, the commonly used terrestrial LiDAR is limited to stationary data collection, which reduces the accessibility to some components of the bridges. To tackle this limitation, a LiDAR attached to an Unmanned Aerial Vehicle (UAV) provides more flexibility and accessibility for inspecting large surface areas without threatening inspectors' safety. After providing a comprehensive literature review about the usage of UAVs and LiDAR for the inspection of different types of structures, this paper proposes a high level framework for bridge inspection using LiDAR-equipped UAV. The framework includes the following steps: (1) planning a collisionfree optimized path with respect to the minimum cost and maximum coverage considering a variety of constrains and requirements related to the hardware and the inspection task, and (2) data analysis for detecting the surface defects based on the collected point clouds.

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Section: Methods For Surface Defects Inspectionmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…information, but also to detect problems in the pavement (cracks and potholes), thus reducing the consumption of time and resources (Koch and Brilakis, 2011).…”

Section: Photogrammetrymentioning

confidence: 99%

The use of geomatic techniques to improve the management of metro infrastructure

et al. 2017

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The management of large infrastructures requires comprehensive knowledge of their general characteristics and the elements that constitute them. It is essential to know which elements are involved and where they are located. At the same time, it is vital to understand the overall geometry. The accuracy and precision required for both types of information is different: much greater accuracy is required in the understanding of the geometry. However, it is difficult to obtain this information about underground infrastructures due to their lighting characteristics, hazards, lack of GNSS (Global Navigation Satellite System) signals, and time available. In this case, to achieve the required quality in both tasks, the mobile mapping systems that are widely used in large surveys and urban inventories have to be adapted to obtain data in underground conditions. To achieve this, considerable geodesic support is necessary. The GNSS positioning system should be replaced by IMU sensors, and infrared sensor cameras are used, among other pieces of equipment. To establish the trajectory, additional information extracted from the targets detected in the laser scanning point clouds must be used. Therefore, a laser scanning and kinematic photogrammetry survey was carried out to obtain the centimetric precision and complete information that is still used today. This paper presents an adaptation of these systems for use in the Ferrocarril Metropolità de Barcelona.

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“…칼라 영상을 그레이 영상으로 만들고 미디언 필터링을 이용 하여 잡음을 제거한다. 그리고 Histogram shape-based thresholding 방법 [3,5] 을 이용하여 이진 영상으로 분할 한다. 이때 영상 분할은 다음 식과 같이 포트홀의 포함 될 수 있는 영역과 배경 영역으로 나눌 수 있다.…”

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“…기존 방법 [3] 에서는 표준 편차의 비교를 더욱 두드러지게 하기 위하여 Leung-Malik [7] , Schmid [8] proposed method existing method [3] number of fault detection 그림 9. 실험 영상에서 포트홀 잘못 검출 개수 Fig.…”

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Pothole Detection Method in Asphalt Pavement

김영로¹,

김태형²,

류승기³

2014

Journal of the Institute of Electronics and Information Enginee

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In this paper, we propose a pothole detection method in asphalt pavement using various features. Segmentation, candidate, and decision steps of pothole detection are processed according to the values which are derived from feature characteristics. Segmentation step, we use histogram and closing operation of morphology filter which extracts dark regions for pothole detection. Candidate step, we extract candidate regions of pothole using various features such as size, compactness, etc. Finally, decision step, candidate regions are decided whether pothole or not using comparison of pothole and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination of pothole and similar patterns.

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Pothole detection in asphalt pavement images

Cited by 476 publications

References 22 publications

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

The use of geomatic techniques to improve the management of metro infrastructure

Pothole Detection Method in Asphalt Pavement

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