Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Non-Contact Evaluation Methods For Infrastructure Condition Assessment, Sattar Dorafshan
Non-Contact Evaluation Methods For Infrastructure Condition Assessment, Sattar Dorafshan
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Civil and Environmental Engineering Faculty Publications
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …