| Title |
Deep Learning based Masonry Wall Defect Classification using a MEMS LiDAR Sensor |
| Authors |
황영서(Hwang, Yeongseo) ; 박근형(Park, Gunhyung) ; 양강혁(Yang, Kanghyeok) |
| DOI |
https://doi.org/10.5659/JAIK.2023.39.1.313 |
| Keywords |
Deep Learning; MEMS LiDAR; 3D Laser Scanner; Masonry Wall; Defect Classification |
| Abstract |
Most of the maintenance and safety inspections of buildings are performed with visual assessment of the inspector, which consumes a lot of
time and cost. With the development of computer vision and digital technologies such as 3D Laser scanners, automatic defect recognition
using image processing and artificial intelligence has been widely studied. Current approach is largely relying on the image obtained from the
camera and the recognition performance could be varied depending on the surrounding environment. Recently, studies using 3D Laser scanner
are being conducted to solve these problems. However, terrestrial laser scanners are expensive, so it is difficult to apply at the construction
site. Therefore, this study proposed a method that can recognize masonry wall defects using a Microelectromechanical systems based Light
Detection and Ranging sensor that having much lower price and reliable performance. This study was performed using masonry wall
structures and data were collected from samples having various types of defects in a laboratory environment. Masonry wall defects were
recognized using ResNet-50 and VGG16 models, which are widely used in previous studies. As a result of the classification, ResNet-50 and
VGG16 achieved 98.75% and 96.88% accuracy, respectively. The results of this study can be utilized in the development of real-time defect
recognition method for a masonry wall at construction sites. |