TY - GEN
T1 - Pavement Crack Detection and Localization using Convolutional Neural Networks (CNNs)
AU - Ali, Luqman
AU - Valappil, Najiya Koderi
AU - Kareem, Daniya Najiha Abdul
AU - John, Mary Josy
AU - Al Jassmi, Hamad
N1 - Funding Information:
The authors are thankful to Dr. Ahmed Agiel for his continued encouragement and support in writing this article through the graduate level course (MECH710: Research Methodologies) at the United Arab Emirates University (UAEU).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Regular inspection and maintenance of roads are required to ensure safe transportation. While examining the state of structural health, cracks are considered as the primary indicators. In the past decades, researchers have been working on various image-based pavement crack detection techniques for non-destructive evaluation. The main advantages of these techniques over manual inspection are accuracy, efficiency and cost. However, the problems associated with the existing methods are their dependence on the handcrafted features, which may not give accurate results due to insufficient feature selection. In this paper, an automatic image-based crack detection algorithm for pavement crack detection using Convolutional Neural Network is proposed. The data set was obtained from various road surfaces of United Arab Emirates (UAE) by using an unmanned aerial vehicles (UAVs) and was used in training and validation of the proposed system. The collected data was also used to create a composite view of the road by creating a continuous mosaic. From the experimental results, it was found that the proposed system has an accuracy of 92% in the validation stage and 90% in the testing stage and can be used for crack detection of road surfaces.
AB - Regular inspection and maintenance of roads are required to ensure safe transportation. While examining the state of structural health, cracks are considered as the primary indicators. In the past decades, researchers have been working on various image-based pavement crack detection techniques for non-destructive evaluation. The main advantages of these techniques over manual inspection are accuracy, efficiency and cost. However, the problems associated with the existing methods are their dependence on the handcrafted features, which may not give accurate results due to insufficient feature selection. In this paper, an automatic image-based crack detection algorithm for pavement crack detection using Convolutional Neural Network is proposed. The data set was obtained from various road surfaces of United Arab Emirates (UAE) by using an unmanned aerial vehicles (UAVs) and was used in training and validation of the proposed system. The collected data was also used to create a composite view of the road by creating a continuous mosaic. From the experimental results, it was found that the proposed system has an accuracy of 92% in the validation stage and 90% in the testing stage and can be used for crack detection of road surfaces.
KW - Civil Inspection
KW - Computer Vision
KW - Convolutional Neural Network (CNN)
KW - Pavement Crack Detection
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U2 - 10.1109/ICD47981.2019.9105786
DO - 10.1109/ICD47981.2019.9105786
M3 - Conference contribution
AN - SCOPUS:85087059309
T3 - Proceeding of 2019 International Conference on Digitization: Landscaping Artificial Intelligence, ICD 2019
SP - 217
EP - 221
BT - Proceeding of 2019 International Conference on Digitization
A2 - Pradhan, Manas Ranjan
A2 - Senior, John
A2 - Shanmugan, Joghee
A2 - Sattar, Omar Hikmat
A2 - Rabik, Sheikh Abdul
A2 - Mago, Beenu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Digitization, ICD 2019
Y2 - 18 November 2019 through 19 November 2019
ER -