Pavement crack image acquisition methods and crack extraction algorithms: A review

Wang, Weixing; Wang, Mengfei; Li, Hongxia; Hong, Zhao; Kevin, Wang; He, Changtao; Wang, Jun; ZHENG, Si-fan; Chen, Jiabin

doi:10.1016/j.jtte.2019.10.001

Cited by 70 publications

(32 citation statements)

References 88 publications

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“…The crack detection in pavement structures at pixel level can also implement the pixel-based crack detection methods in concrete structures such as edge detection, filtering, thresholding, and other morphological techniques. 59 Cheng et al 60 determined the real-time thresholding from image intensities by using sample space reduction and interpolation approach and extracted the cracks by using image thresholding-based segmentation. Zou et al 61 developed the CrackTree to perform crack detection in pixel level.…”

Section: Cv-shm-llmentioning

confidence: 99%

“…For example, qualified engineers and inspectors implement hammer sounding and/or chain drag, and visual inspection for concrete bridge deck evaluations, yet these methods require substantial field labor, experience, and operational interruptions. Based on visual inspections, National Bridge Inventory (NBI) defined the condition rating categories to evaluate three primary components of a bridge: deck, superstructure, and substructure (Items 58,59,60). The condition rating categories of NBI are divided into 10, from 0 to 9, and inspectors rate the bridge condition subjectively based on their experiences in accordance with the descriptions provided by US Department of Transportation (USDOT) 1 and Federal Highway Administration (FHWA).…”

Section: Introductionmentioning

confidence: 99%

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A review of computer vision–based structural health monitoring at local and global levels

Dong

Çatbaş

2020

Structural Health Monitoring

327

130

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Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application advantages such as non-contact, long distance, rapid, low cost and labor, and low interference to the daily operation of structures, it is promising to consider computer vision–structural health monitoring as a complement to the conventional structural health monitoring. This article presents a general overview of the concepts, approaches, and real-life practice of computer vision–structural health monitoring along with some relevant literature that is rapidly accumulating. The computer vision–structural health monitoring covered in this article at local level includes applications such as crack, spalling, delamination, rust, and loose bolt detection. At the global level, applications include displacement measurement, structural behavior analysis, vibration serviceability, modal identification, model updating, damage detection, cable force monitoring, load factor estimation, and structural identification using input–output information. The current research studies and applications of computer vision–structural health monitoring mainly focus on the implementation and integration of two-dimensional computer vision techniques to solve structural health monitoring problems and the projective geometry methods implemented are utilized to convert the three-dimensional problems into two-dimensional problems. This review mainly puts emphasis on two-dimensional computer vision–structural health monitoring applications. Subsequently, a brief review of representative developments of three-dimensional computer vision in the area of civil engineering is presented along with the challenges and opportunities of two-dimensional and three-dimensional computer vision–structural health monitoring. Finally, the article presents a forward look to the future of computer vision–structural health monitoring.

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Section: Cv-shm-llmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of computer vision–based structural health monitoring at local and global levels

Dong

Çatbaş

2020

Structural Health Monitoring

327

130

View full text Add to dashboard Cite

show abstract

“…After then, the accuracy for particular situations, the robustness for a wide range of scenarios, time efficiency, and integration into higher-order tasks define visual lane detection and tracking as the continuing research subject [9]. At present, this kind of lane line marking detection algorithms based on Machine vision and Image processing can be divided into two categories: the first kind of algorithms are feature-based, which is the similar to the road detection in an aerial or a remote-sensing image [28] or the similar to the crack detection in a pavement image [27], such as crack detection by Steger and Hydrodynamics with improved Fractional differential [26], Crack detection in shadowed images on gray level deviations in a moving window [25]; and the second kind of algorithms are model-based [34]. The early warning model of lane departure decision based on taking the current position of driving vehicle in lane as the decision basis to analyze whether lane departure will occur.…”

Section: Introductionmentioning

confidence: 99%

Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix

Wang¹,

Berholm²,

Hu³

et al. 2021

ITC

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To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detecting point within the pre-set valid detection region; and (4) After the noise removal based on the minimum circumscribed rectangles, the candidate points of lane lines are connected as segments, and for the gap filling between segments, in order to make connection correctly, the algorithm makes the filling in two steps, short gap and long gap fillings, and the long gap filling is made on the combination of segment angle difference and gap distance and gap angle. By testing hundreds of images of the lane lines at raining weather and by comparing several traditional image enhancement and segmentation algorithms, the new method of the lane line detection can produce the satisfactory results.

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“…The phenomenon of the deformations presents a big problem on the runways, which can be abolished with early precautions when dealing with airport infrastructure. The automatized obtainment and spatial data processing of information about the deformations on the road surfaces and on the runways are the subject of many kinds of recent research [14][15][16][17][18][19][20][21][22]. The authors of the research mostly analyze the recordings of the roadways and runways which are a product of remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Development of the Model for Monitoring the Deformations on the Airport Runways

2021

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The demand for safety and to provide safety are increasing, parallel with the growth of the need for mobility, transport and logistics. A big part of the demands and recommendations to provide safety in air traffic is related to the safety at and in the surroundings of the runways. The research focused on exploring the airport infrastructure; mainly with the aim of detecting and monitoring the deformations (cracks, displacements, etc.) of the runways which are causally connected with unusual landings or taking offs, with trips from the runways and with loss of control over the aircraft during the landings, taking offs and while moving the runways or taxiways, etc., and researching their effect on providing safety and the efficiency of the airports’ function. The research was executed at the Maribor Edvard Rusjan Airport in three phases; the first two were meant for geodetic measurements to determine vertical deformations and the third for supplementary measurements to determine static load capacity. The result of the research is an innovative model for the continuous monitoring of the deformations on the runways. The suggested model enables determination and display of the areas of the vertical deviations which are impossible to detect with a visual check-up, joining the supplementary methods and assessing the condition of the runway based on executed measurements.

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Pavement crack image acquisition methods and crack extraction algorithms: A review

Cited by 70 publications

References 88 publications

A review of computer vision–based structural health monitoring at local and global levels

A review of computer vision–based structural health monitoring at local and global levels

Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix

Optimization and Development of the Model for Monitoring the Deformations on the Airport Runways

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