Bridge Girder Crack Assessment Using Faster RCNN Inception V2 and Infrared Thermography

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.

Review of artificial intelligence-based bridge damage detection

“…Infrared thermography can overcome the limitations of ordinary images and can be used to identify damage in dark areas or inside structures. Qurishee et al 99 used Faster R-CNN and infrared thermography for bridge crack assessment. They used Inception V2 as the feature extraction module for Faster R-CNN.…”