Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera.

Hyuntae Bang,Jiyoung Min,Haemin Jeon

doi:10.3390/s21082759

Abstract

Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, is one of the important parameters that can be used to estimate the condition of the structure. In this paper, deep learning-based detection and quantification of structural damage using structured lights and a depth camera is proposed. The proposed monitoring system is composed of four lasers and a depth camera. The lasers are projected on the surface of the structures, and the camera captures images of the structures while measuring distance. By calculating an image homography, the captured images are calibrated when the structure and sensing system are not in parallel. The Faster RCNN (Region-based Convolutional Neural Network) with Inception Resnet v2 architecture is used to detect three types of surface damage: (i) cracks; (ii) delamination; and (iii) rebar exposure. The detected damage is quantified by calculating the positions of the projected laser beams with the measured distance. The experimental results show that structural damage was detected with an F1 score of 0.83 and a median value of the quantified relative error of less than 5%.

Highlights

This paper describes a deep learning-based structural damage detection and quantification method using structured lights and a depth camera
The projected laser beams are captured by the depth camera along with distance
Since the accuracy of quantification is directly related to the position of the projected laser beams, the initial inclination angles of lasers are calculated using a specially designed jig module

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Due to the increase in aging structures and a dearth of construction labor, various attempts have been made to replace the current inspector-centered exterior monitoring method. With the rapid development of computer vision and camera hardware technologies, vision-based structural health monitoring techniques have become a subject of active research. Vision-based structural damage monitoring methods can detect and quantify damage using consistent and reliable criteria through image processing techniques. When it is applied to a mobile platform such as a drone, it is possible to access various parts of large civil structures that are difficult for inspectors to access

Methods

Results

Conclusion