Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks-Reference-Cited by-同舟云学术

Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks

Published:2019-11 Issue:6 Volume:33 Page:04019040
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Alipour Mohamad¹^ORCID,Harris Devin K.²^ORCID,Miller Gregory R.³^ORCID

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742. ORCID: .

3. Professor, Dept. of Civil, Environmental and Infrastructure Engineering, Univ. of Washington, P.O. Box 352700, Seattle, WA 98195-2700. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000854

Reference49 articles.

1. Analysis of Edge-Detection Techniques for Crack Identification in Bridges

2. Adaptive Thresholding using the Integral Image

3. A Computational Approach to Edge Detection

4. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

5. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

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