TITLE:
Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography
AUTHORS:
Murad Al Qurishee, Weidong Wu, Babatunde Atolagbe, Joseph Owino, Ignatius Fomunung, Said El Said, Sayed Mohammad Tareq
KEYWORDS:
Bridge Girder, Convolution Neural Network, Crack Detection, Structural Health Monitoring, Infrared Thermography
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.10 No.2,
March
16,
2020
ABSTRACT: Manual inspections of infrastructures such as
highway bridge, pavement, dam, and multistoried garage ceiling are time
consuming, sometimes can be life threatening, and costly. An automated
computerized system can reduce time, faulty inspection, and cost of inspection.
In this study, we developed a computer model using deep learning Convolution
Neural Network (CNN), which can be used to automatically detect the crack and
non-crack type structure. The goal of this research is to allow application of
state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV)
technologies for highway bridge girder inspection. As a pilot study of
implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old
garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking
garage ceiling using handheld mobile phone and drone. After training the model
shows 98% accuracy with crack and non-crack types of structures.