@inproceedings{6a082a5b076d4481b8d69478bf693be1,
title = "Artificial intelligence and signal processing for infrastructure assessment",
abstract = "The Ground Penetrating Radar (GPR) is being recognized as an effective nondestructive evaluation technique to improve the inspection process. However, data interpretation and complexity of the results impose some limitations on the practicality of using this technique. This is mainly due to the need of a trained experienced person to interpret images obtained by the GPR system. In this paper, an algorithm to classify and assess the condition of infrastructures utilizing image processing and pattern recognition techniques is discussed. Features extracted form a dataset of images of defected and healthy slabs are used to train a computer vision based system while another dataset is used to evaluate the proposed algorithm. Initial results show that the proposed algorithm is able to detect the existence of defects with about 77\% success rate.",
keywords = "Ground Penetrating Radar, image processing and pattern recognition",
author = "Khaled Assaleh and Tamer Shanableh and Sherif Yehia",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015 ; Conference date: 09-03-2015 Through 12-03-2015",
year = "2015",
doi = "10.1117/12.2179920",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Shull, \{Peter J.\}",
booktitle = "Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015",
address = "United States",
}