An image-based system for asphalt pavement bleeding inspection

The pavement distress information is essential for evaluating the pavement. Automatic pavement inspection systems can be so beneficial in providing needed information on pavement conditions. The bl...

[1]  Fereidoon Moghadas Nejad,et al.  Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review , 2017 .

[2]  Tao Ma,et al.  Intelligent decision model of road maintenance based on improved weight random forest algorithm , 2020, International Journal of Pavement Engineering.

[3]  Zheng Tong,et al.  Pavement defect detection with fully convolutional network and an uncertainty framework , 2020, Comput. Aided Civ. Infrastructure Eng..

[4]  Yunyi Jia,et al.  Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method , 2020 .

[5]  Xilong Qu,et al.  CT Image Denoising Using Double Density Dual Tree Complex Wavelet with Modified Thresholding , 2018, 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA).

[7]  Mingxuan Sun,et al.  Road profile reconstruction using connected vehicle responses and wavelet analysis , 2018 .

[8]  Symeon E. Christodoulou,et al.  Vision- and Entropy-Based Detection of Distressed Areas for Integrated Pavement Condition Assessment , 2019, J. Comput. Civ. Eng..

[9]  Ahsan Ali,et al.  Performance assessment of Kinect as a sensor for pothole imaging and metrology* , 2018 .

[10]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Fereidoon Moghadas Nejad,et al.  Evaluation of pavement surface drainage using an automated image acquisition and processing system , 2018 .

[14]  Kelvin C. P. Wang,et al.  Automatic classification of pavement crack using deep convolutional neural network , 2018, International Journal of Pavement Engineering.

[15]  Alice J. Kozakevicius,et al.  Pothole Detection in Asphalt: An Automated Approach to Threshold Computation Based on the Haar Wavelet Transform , 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC).

[16]  Kelvin C. P. Wang,et al.  Friction-ResNets: Deep Residual Network Architecture for Pavement Skid Resistance Evaluation , 2020 .

[17]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[18]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[19]  Fereidoon Moghadas Nejad,et al.  Automatic image acquisition with knowledge-based approach for multi-directional determination of skid resistance of pavements , 2016 .

[20]  Siddhartha Kumar Khaitan,et al.  Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection , 2017 .

[21]  Kaige Zhang,et al.  Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning , 2018, J. Comput. Civ. Eng..

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Enmin Song,et al.  Texture Features and Image Texture Models , 2019, Image Texture Analysis.

[24]  Amir Golroo,et al.  Road roughness measurement using a cost-effective sensor-based monitoring system , 2019, Automation in Construction.

[25]  Xiaowei Luo,et al.  An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement , 2020, Automation in Construction.

[26]  Fereidoon Moghadas Nejad,et al.  Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection , 2016 .

[27]  Ala R. Abbas,et al.  Wavelet-based characterisation of asphalt pavement surface macro-texture , 2014 .

[28]  Fereidoon Moghadas Nejad,et al.  The Hybrid Method and its Application to Smart Pavement Management , 2013 .

[29]  Ausif Mahmood,et al.  A Framework for Designing the Architectures of Deep Convolutional Neural Networks , 2017, Entropy.

[30]  B. Koosha,et al.  An analytical–empirical investigation of the bleeding mechanism of asphalt mixes , 2013 .

[31]  Liang Song,et al.  Faster region convolutional neural network for automated pavement distress detection , 2019, Road Materials and Pavement Design.

[32]  Patricio A. Vela,et al.  Automated Pavement Patch Detection and Quantification Using Support Vector Machines , 2018, J. Comput. Civ. Eng..

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Jian Zhou,et al.  Wavelet-based pavement distress detection and evaluation , 2003 .

[35]  Behrouz Mataei,et al.  An Overview of Multiresolution Analysis for Nondestructive Evaluation of Pavement Surface Drainage , 2019 .

[36]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network , 2018, Comput. Aided Civ. Infrastructure Eng..

[37]  Guohui Zhang,et al.  A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition , 2018, IEEE Transactions on Intelligent Transportation Systems.

[38]  Wei Jiang,et al.  Convolutional neural network for pothole detection in asphalt pavement , 2019, Road Materials and Pavement Design.

[39]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .

[40]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[41]  Kris De Brabanter,et al.  Wavelet Filter Design for Pavement Roughness Analysis , 2016, Comput. Aided Civ. Infrastructure Eng..

[42]  Amir Golroo,et al.  Low-cost infrared-based pavement roughness data acquisition for low volume roads , 2020 .

[43]  Yoshihide Sekimoto,et al.  Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images , 2018, Comput. Aided Civ. Infrastructure Eng..

[44]  Vinícius M. A. de Souza,et al.  Asphalt pavement classification using smartphone accelerometer and Complexity Invariant Distance , 2018, Eng. Appl. Artif. Intell..

[45]  P. M. K. Prasad,et al.  Biorthogonal Wavelet-based Image Compression , 2018 .

[46]  Yashon O. Ouma,et al.  Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform , 2016, Adv. Eng. Informatics.

[47]  Fereidoon Moghadas Nejad,et al.  An expert system based on wavelet transform and radon neural network for pavement distress classification , 2011, Expert Syst. Appl..

[48]  Fitri Utaminingrum,et al.  Road surface classification based on LBP and GLCM features using kNN classifier , 2020 .

[49]  Kelvin C. P. Wang,et al.  Wavelet based macrotexture analysis for pavement friction prediction , 2018 .

[50]  Xiaorong Wang,et al.  Effect of ambient condition on n-heptane droplet evaporation , 2017 .

[51]  Yashon O. Ouma,et al.  Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction , 2017 .

[52]  Mustafa Karaşahin,et al.  Determination of seal coat deterioration using image processing methods , 2014 .

[53]  Nhat-Duc Hoang,et al.  Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression , 2019, Automation in Construction.

[54]  Fereidoon Moghadas Nejad,et al.  An image-based system for pavement crack evaluation using transfer learning and wavelet transform , 2020, International Journal of Pavement Research and Technology.

[55]  Steve Vanlanduit,et al.  Fiber Optics Sensors in Asphalt Pavement: State-of-the-Art Review , 2019, Infrastructures.

[56]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[57]  Jie Gao,et al.  Recognition of asphalt pavement crack length using deep convolutional neural networks , 2018 .

[58]  Y. Miao,et al.  Characterizing Asphalt Pavement 3-D Macrotexture Using Features of Co-occurrence Matrix , 2015 .

[59]  Philippe Bolon,et al.  2-D Wavelet Packet Spectrum for Texture Analysis , 2013, IEEE Transactions on Image Processing.

[60]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[61]  Robert J. Thomas,et al.  Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete , 2018, Construction and Building Materials.

[62]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[63]  Li He,et al.  Automatic pavement defect detection using 3D laser profiling technology , 2018, Automation in Construction.

[64]  Fengxiang Qiao,et al.  Wavelet Analysis to Characterize the Dependency of Vehicular Emissions on Road Roughness , 2017 .