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Asphalt-surface defects detection, based on Tyre/Road noise analysis and geo-processing
2019
Proceedings of the ICA congress
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In this probe of concept, a group of four local and distributed road-surface defects were automatically detected and plotted on map. ...
A new approach to detect different asphalt defectology based on tyre/road noise analysis by Machine Learning algorithms is proposed. ...
ACKNOWLEDGEMENTS Part of this research project was founded by the Granth: "Convocatoria Abierta 2017" -SENESCYT form Ecuadorian Government, received by the author C. Ramos-Romero. ...
doi:10.18154/rwth-conv-238915
fatcat:f5t25me3hzgxpoanzome7dabha
Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks
2018
Advances in Civil Engineering
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Effective road maintenance requires adequate periodic surveys of asphalt pavement condition. The manual process of pavement assessment is labor intensive and time-consuming. ...
This study proposes an alternative for automating the periodic surveys of pavement condition by means of image processing and machine learning. ...
AdaBoost
The Dataset of Asphalt Pavement Images Since Adaboost CTree is a supervised learning approach, a dataset of pavement images with the prespecified ground truth categories must be prepared for ...
doi:10.1155/2018/5989246
fatcat:bcakcoktb5ajzbk3x7n3mnzahu
An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction
2018
Advances in Civil Engineering
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This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. ...
A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM ...
Because ANN and LS-SVM are supervised learning algorithms, a data set of asphalt pavement images with ground truth conditions of pothole and nonpothole has to be collected for model training and validation ...
doi:10.1155/2018/7419058
fatcat:ckqoku4mxfch5f62f432nky7m4
A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning
2022
Advances in Civil Engineering
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During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. ...
This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. ...
Herein, we focus on previous works that employ image-processing techniques, image texture analyses, and machine learning models for automatic detection of asphalt pavement distresses, especially pavement ...
doi:10.1155/2022/9193511
fatcat:zng27yel3ngfjdyom7i3iplt74
Computer Vision-Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic-Based Investigation Metaheuristic
2021
Complexity
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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. ...
This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. ...
model used for automatic detection of the pothole and patched pothole. e model is an integration of image processing techniques, supervised machine learning-based data classification, and metaheuristic ...
doi:10.1155/2021/3511375
fatcat:chhd26y66rfsnhxq26fr32zik4
Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance
2018
Mathematical Problems in Engineering
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This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. ...
Periodic surveys of asphalt pavement condition are very crucial in road maintenance. ...
Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this research work. ...
doi:10.1155/2018/6290498
fatcat:5lv5ojfr6nazta5hnwksntnxvq
Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review
2022
Sensors
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The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the ...
With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. ...
This research is also supported by the Ontario Ministry of Colleges and Universities' Early Researcher Award funding provided to the corresponding author. ...
doi:10.3390/s22083044
pmid:35459034
pmcid:PMC9029655
fatcat:pqrm3lcihzfv5n6ojjvxg7plkq
Development of a Multi-Sensor System for Road Condition Mapping
2014
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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High resolution RGB imaging enables automatic asphalt crack detection and provides base images for spectrometry spots. ...
Infrared spectrometry is employed to monitor the deterioration of the surface material and pavement condition, in particular by aging. ...
A backpropagation algorithm was applied in order to achieve supervised learning. The overall accuracy of the calculated confusion matrix was 90 -100% (Segl et al., 2003) . ...
doi:10.5194/isprsarchives-xl-1-265-2014
fatcat:4qjzyuthbrcyrgzwtybh7g73dm
ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation
2022
Sensors
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This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this ...
The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. ...
Acknowledgments: The authors would like to thank Paul Ficau for the support provided during the field survey for PCI evaluation and Elena Rantica (Scientific Dissemination Working Group of CNR-IIA) for ...
doi:10.3390/s22093414
pmid:35591108
pmcid:PMC9101772
fatcat:avq5vzgsy5eoplpyjyhfrjh23m
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
2015
Advanced Engineering Informatics
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tunnels, underground concrete pipes, and asphalt pavements. ...
of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. ...
[27] [20] and Kawasue et al. [89] .
Asphalt pavements 3.4.1 Pre-processing To automatically detect distress on pavement images, it is required to perform some preprocessing of the images. ...
doi:10.1016/j.aei.2015.01.008
fatcat:6rvl3hojqbddznjphjxq4tso5y
Neural Network Based Pavement Condition Assessment with Hyperspectral Images
2020
Remote Sensing
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Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent ...
Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. ...
Acknowledgments: We are grateful to the Republic of Turkey Ministry of Industry and Technology for providing the project funding. ...
doi:10.3390/rs12233931
fatcat:xkidg7reend5bglttn4usnizcy
Automated Detection of Multiple Pavement Defects
2017
Journal of computing in civil engineering
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the longitudinal and transverse profiles of the 50 pavement, the condition at its edges, and the texture of the surface. ...
Several US states own such vehicles for 54 automatically detecting pavement data (Attoh-Okine and Adarkwa 2013; Liosatos 2013; Rami 55 and Kim 2015; Richardson et al. 2015; Rick Miller 2015; Zhou et al ...
"Supervised machine learning: A 604 review of classification techniques." 605
). ...
doi:10.1061/(asce)cp.1943-5487.0000623
fatcat:6p5na3rbr5bc3cycy6o3tnrnaq
Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach
2018
Computational Intelligence and Neuroscience
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To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. ...
Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying. ...
Because LSSVM belongs to the category of a supervised learning approach, a dataset of pavement images with ground truth surface conditions must be prepared for training and validating phases. us, this ...
doi:10.1155/2018/1312787
fatcat:aribx2s3crcpvoy2qdh2e77wvu
Methodology for Classifying the Structural State of Uninspected Pipes in Sewer Networks Based on Support Vector Machines
2021
Ingeniería e Investigación
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of pipes; and (iii) provided a guide for establishing the number or percentage of pipes that require inspection or intervention. ...
This paper presents a method for predicting and classifying the structural state of uninspected sewer pipes using Support Vector Machines, based on the physical characteristics, age, and geographical location ...
Acknowledgements This work was supported by the Administrative Department of Science, Technology, and Innovation (COLCIENCIAS) and Pontificia Universidad Javeriana (Ph.D. grant -Call 727 of 2015: Support ...
doi:10.15446/ing.investig.v42n2.85917
fatcat:7go3inhmuneglaihx45mdqj5fi
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
2020
Sensors
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Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. ...
After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. ...
subdivided images of asphalt pavement. ...
doi:10.3390/s20102778
pmid:32414205
pmcid:PMC7294417
fatcat:sl3t5pxt3rhy3adzjgziumvmia
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