Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

Onuorah, Chinedum Anthony; Hutton-Mckenzie, Angus; Chaychian, Sara; Sun, Yichuang; Siau, Johann

doi:10.1109/jsen.2018.2812787

Cited by 7 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IMU installed on the detection vehicle can detect whether the road su damaged by analyzing the data of the acceleration sensor, angular velocity sen other components [60][61][62], as shown in Figure 7. Acceleration changes, angular changes, and attitude changes in the process of vehicle movement can all be coll IMU, which requires a small amount of calculation and can achieve high accuracy M. et al [66] presented a road surface defect identification system based on 3 erometers, a global positioning system (GPS), and video modules deployed on v Anthony et al [67] applied IMU and laser to evaluate road roughness and gener profile, improving the accuracy of vehicle-based longitudinal road profilometer roughness and large texture evaluation. Christodoulou et al [68] inputted acce and angular velocity data into the designed artificial neural network to distingu holes on the road, and the detection accuracy was about 90%.…”

Section: Imumentioning

confidence: 99%

“…M. et al [66] presented a road surface defect identification system based on 3D accelerometers, a global positioning system (GPS), and video modules deployed on vehicles. Anthony et al [67] applied IMU and laser to evaluate road roughness and generate road profile, improving the accuracy of vehicle-based longitudinal road profilometer in road roughness and large texture evaluation. Christodoulou et al [68] inputted acceleration and angular velocity data into the designed artificial neural network to distinguish potholes on the road, and the detection accuracy was about 90%.…”

Section: Section Summarymentioning

confidence: 99%

See 1 more Smart Citation

Review of Intelligent Road Defects Detection Technology

Zhou¹,

Guo

Hou³

et al. 2022

Sustainability

View full text Add to dashboard Cite

Road defects are important factors affecting traffic safety. In order to improve the identification efficiency of road diseases and the pertinence of maintenance and management, intelligent detection technologies of road diseases have been developed. The problems of high cost and low efficiency of artificial inspection of road diseases are solved efficiently, and the quality of road construction is improved availably. This is not only the guarantee of highway quality but also the guarantee of people’s lives and safety. This study focuses on the intelligent detection of road disease and summarizes the commonly used detection equipment in the intelligent detection technology of road diseases, which include cameras, GPR, LiDAR, and IMU. It systematically describes the evolution and development of road disease detection technology. This study analyzes the common problems existing in road disease detection technology and proposes corresponding improvement suggestions. Finally, the development trend of road detection technology is discussed, which has practical significance for the future development of road detection technology.

show abstract

Section: Imumentioning

confidence: 99%

Section: Section Summarymentioning

confidence: 99%

Review of Intelligent Road Defects Detection Technology

Zhou¹,

Guo

Hou³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…With the development of measurement sensors, various technological solutions have been applied to automatically detect and report pavement distress and performance indicators information for government agencies in order to accelerate pavement maintenance and management tasks. Typical asphalt pavement distresses—e.g., cracks [ 1 , 2 , 3 ], potholes [ 4 , 5 ], rutting [ 5 , 6 ]—often cause significant safety and economic problems [ 1 , 7 , 8 , 9 ], while some pavement performance indicators including road markings [ 10 ], textures [ 11 , 12 ], and grooves [ 13 , 14 ] have great significance for traffic safety. Moreover, pavement distress and performance indicators are important foundations of intelligent transport systems [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis

Gui

Zhang

et al. 2018

Sensors

View full text Add to dashboard Cite

High-precision 3D laser scanning pavement data contains rich pavement scene information and certain components associations. Moreover, for pavement maintenance and management, there is an urgent need to develop automatic methods that can extract comprehensive information about different pavement indicators simultaneously. By analyzing the frequency and sparse characteristics of pavement distresses and performance indicators—including the cracks, road markings, rutting, potholes, textures—this paper proposes 3D pavement components decomposition model (3D-PCDM) which decomposes the 3D pavement profiles into sparse components x, low-frequency components f, and vibration components t. Designed high-pass filter was first employed to separate f, then, x and t are separated by total variation de-noising which based on sparse characteristics. Decomposed x can be used to characterize the location and depth information of sparse and sparse derived signals such as cracks, road marks, grooves, and potholes in profiles. Decomposed f can be used to determine the slow deformation of pavement. While decomposed t reflects the fluctuation of the pavement material particles. Experiments were conducted using actual pavement 3D data, the decomposed components can obtain by 3D-PCDM. The effectiveness and accuracy of the x are verified by actual cracks and road markings, the accuracy of extracted sparse components is over 92.75%.

show abstract

Research progress of laser triangulation on-machine measurement technology for complex surface: A review

Ding

Huang

et al. 2023

Measurement

View full text Add to dashboard Cite

Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

Cited by 7 publications

References 22 publications

Review of Intelligent Road Defects Detection Technology

Review of Intelligent Road Defects Detection Technology

A Component Decomposition Model for 3D Laser Scanning Pavement Data Based on High-Pass Filtering and Sparse Analysis

Research progress of laser triangulation on-machine measurement technology for complex surface: A review

Contact Info

Product

Resources

About