Deep learning-based multi-class damage detection for autonomous post-disaster reconnaissance
Journal
Structural Control and Health Monitoring
Journal Volume
27
Journal Issue
4
Date Issued
2020
Author(s)
Abstract
Timely assessment of damages induced to buildings due to an earthquake is critical for ensuring life safety, mitigating financial losses, and expediting the rehabilitation process as well as enhancing the structural resilience where resilience is measured by an infrastructure's capacity to restore full functionality post extreme events. Since manual inspection is expensive, time consuming and risky, low-cost unmanned aerial vehicles or robots can be leveraged as a viable alternative for quick reconnaissance. Visual data captured by the sensors mounted on the robots can be analyzed, and the damages can be detected and classified autonomously. The present study proposes the use of deep learning-based approaches to this end. Region-based convolutional neural network (Faster RCNN) is exploited to detect four different damage types, namely, surface crack, spalling (which includes fa?ade spalling and concrete spalling), and severe damage with exposed rebars and severely buckled rebars. The performance of the proposed approach is evaluated on manually annotated image data collected from reinforced concrete buildings damaged under several past earthquakes such as Nepal (2015), Taiwan (2016), Ecuador (2016), Erzincan (1992), Duzce (1999), Bingol (2003), Peru (2007), Wenchuan (2008), and Haiti (2010). Several experiments are presented in the paper to illustrate the capabilities, as well as the limitations, of the proposed approach for earthquake reconnaissance. It was observed that Inception-ResNet-v2 significantly outperforms the other networks considered in this study. The research outcome is a stepping stone forward to facilitate the autonomous assessment of buildings where this can be potentially useful for insurance companies, government agencies, and property owners. ? 2020 John Wiley & Sons, Ltd.
Subjects
Aircraft detection
Antennas
Concrete buildings
Costs
Damage detection
Earthquakes
Insurance
Losses
Neural networks
Reinforced concrete
Spalling
Unmanned aerial vehicles (UAV)
Concrete spalling
Convolutional neural network
Earthquake reconnaissance
Faster RCNN
Government agencies
Insurance companies
Learning-based approach
Post disaster reconnaissance
Deep learning
SDGs
Type
journal article