Paper Info
Title
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types
Abstract
AbstractAbstractComputer vision‐based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real‐time simultaneous detection of multiple types of damages, a Faster Region‐based Convolutional Neural Network (Faster R‐CNN)‐based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R‐CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R‐CNN is evaluated and demonstrated using 11 new 6,000 × 4,000‐pixel images taken of different structures. Its performance is also compared to that of the traditional CNN‐based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real‐time damage detection on video using the trained networks is developed.
Year
DOI
Venue
2018
10.1111/mice.12334
Periodicals
Field
DocType
Volume
Computer vision,Visual inspection,Convolutional neural network,Robustness (computer science),Artificial intelligence,Pixel,Engineering,Deep learning
Journal
33
Issue
ISSN
Citations 
9
1093-9687
26
PageRank 
References 
Authors
1.18
18
5
Name
Order
Citations
PageRank
Young-Jin Cha11438.06
Wooram Choi21054.26
Gahyun Suh3261.18
Sadegh Mahmoudkhani4261.18
Oral Buyukozturk51427.54