Road pavement rut detection using mobile and static terrestrial laser scanning

Shatnawi, Nawras; Obaidat, Mohammed Taleb; Al-Mistarehi, Bara’ W.

doi:10.1007/s12518-021-00400-4

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…where (x Q , y Q , z Q ) is the world coordinate of the observation point, (u (1)Q , v (1)Q ) is the actual image coordinate of the observation point, and (x 1 , y 1 , z 1 ) is the world coordinate of the camera. Substituting Equation (13) into Equation ( 12), the baseline remains B because the actual spatial coordinates do not change. In conjunction with Equation (2), (x Q , y Q , z Q ) can be derived by the following: 14), the generalized equation for UAVs' altitude holding altitude accuracy control can be written as follows:…”

Section: Controlling Precision Analysismentioning

confidence: 99%

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LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

Zhou

Gong

et al. 2022

Drones

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Identifying and detecting the loading size of heavy-duty railway freight cars is crucial in modern railway freight transportation. Due to contactless and high-precision characteristics, light detection and ranging-assisted unmanned aerial vehicle stereo vision detection is significant for ensuring out-of-gauge freight transportation security. However, the precision of unmanned aerial vehicle flight altitude control and feature point mismatch significantly impact stereo matching, thus affecting the accuracy of railway freight measurement. In this regard, the altitude holding control strategy equipped with a laser sensor and SURF_rBRIEF image feature extraction and matching algorithm are proposed in this article for railway freight car loading size measurement. Moreover, an image segmentation technique is used to quickly locate and dismantle critical parts of freight cars to achieve a rapid 2-dimension reconstruction of freight car contours and out-of-gauge detection. The robustness of stereo matching has been demonstrated by external field experiment. The precision analysis and fast out-of-gauge judgment confirm the measurement accuracy and applicability.

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Section: Controlling Precision Analysismentioning

confidence: 99%

“…Duan et al [12] proposed a method to reconstruct shield tunnel lining using point clouds. Shatnawi et al [13] realized the detection of road ruts using laser scanning. The laser scanner has achieved good performance in an austere Automatic out-of-gauge detection is urgently required to replace manual detection.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

Zhou

Gong

et al. 2022

Drones

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“…Pauly et al ( 9 ) investigated how deep learning-based methods may affect several factors in pavement distress detection. Image-based mobile mapping has a major impact on conventional transportation surveying and mapping, such as modelling and estimation of road boundaries in road safety assistance ( 10 – 12 ). This significant impact exists because images contain rich color and texture information, which benefits road extraction and automated distress detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Road Pavement Rutting Using Artificial Neural Network and Conventional Measurements

Shatnawi

Obaidat

Al-Sharideah

2022

Transportation Research Record: Journal of the Transportation R

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Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. This study assists the development of statistical and Artificial pavement rutting models. The proposed methodology is reliable, time-saving, cost-saving, and comfortable. The suggested technique to anticipate rutting considers traffic volumes, pavement, and geometrical parameters such as lane and shoulder widths. This research modeled 33 main highways' ruts. Most of these roads have serious de-stressing problems with rutted pavement. The developed rutting prediction models demonstrated a medium to high correlation between rut depth and independent variables including annual average daily traffic, truck fleet percentage, pavement thickness, and number of lanes. The correlation coefficients such as R2 were found to be moderate for most of the developed models. The linear models of rutting prediction were statistically significant, with R2 values averaging around 66%, whereas the logistic regression model was the best developed rutting model, with an R2 value of 67%, when all variables, including traffic, pavement, and geometry, were considered. Nonlinear models with an R2 value of 57% were used to get similar findings. The artificial neural network (ANN) has been used in this study to model rut depth with same independent variables and gave higher results with R2 value of 82%. The findings showed that an ANN outperformed regression modeling in predicting the depth of a rut.

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“…The results showed that close-range photogrammetry and terrestrial laser scanning achieved the highest precision with total RMSE equal to 1.1 cm and 2.4 cm, respectively. Shatnawi et al (2021) investigated the possibility and the accuracy of measuring rut depth for road pavement using mobile and terrestrial laser scanning systems. Accuracy evaluation was done with reference to filed measurements using GPS.…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Comparative analysis of Road Scanning Techniques

et al. 2023

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A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.

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Road pavement rut detection using mobile and static terrestrial laser scanning

Cited by 8 publications

References 23 publications

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

Modeling Road Pavement Rutting Using Artificial Neural Network and Conventional Measurements

Comparative analysis of Road Scanning Techniques

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