Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

[1]  Tadashi Suetsugi,et al.  PREDICTION OF SEDIMENT YIELD IN AN UNGAUGED BASIN UNDER THE IMPACT OF CASCADE DAM-RESERVOIRS DEVELOPMENT , 2014 .

[2]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[3]  Youngtae Jo,et al.  Pothole Detection System Using a Black-box Camera , 2015, Sensors.

[4]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[5]  Hojjat Adeli,et al.  A novel machine learning‐based algorithm to detect damage in high‐rise building structures , 2017 .

[6]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[7]  Yozo Fujino,et al.  Concrete Crack Detection by Multiple Sequential Image Filtering , 2012, Comput. Aided Civ. Infrastructure Eng..

[8]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[9]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[11]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[12]  Diego A. Casas-Avellaneda,et al.  Detection and localization of potholes in roadways using smartphones , 2016 .

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

[14]  Takuro Yonezawa,et al.  Road marking blur detection with drive recorder , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[15]  Mehmet Karakose,et al.  A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation , 2016 .

[16]  Akira Kawamura,et al.  AN EFFECTIVE SURFACE INSPECTION METHOD OF URBAN ROADS ACCORDING TO THE PAVEMENT MANAGEMENT SITUATION OF LOCAL GOVERNMENTS , 2013 .

[17]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[18]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[19]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[20]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[21]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[22]  Pang-jo Chun,et al.  ASPHALT PAVEMENT CRACK DETECTION USING IMAGE PROCESSING AND NAÏVE BAYES BASED MACHINE LEARNING APPROACH , 2015 .

[23]  Rih-Teng Wu,et al.  A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces , 2017, Comput. Aided Civ. Infrastructure Eng..

[24]  Vikram Pakrashi,et al.  Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces , 2014, Comput. Aided Civ. Infrastructure Eng..

[25]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..