Most of the maintenance and safety inspections of buildings are performed with visual assessment of the inspector, which consumes a lot oftime and cost. With the development of computer vision and digital technologies such as 3D Laser scanners, automa...
Most of the maintenance and safety inspections of buildings are performed with visual assessment of the inspector, which consumes a lot oftime and cost. With the development of computer vision and digital technologies such as 3D Laser scanners, automatic defect recognitionusing image processing and artificial intelligence has been widely studied. Current approach is largely relying on the image obtained from thecamera and the recognition performance could be varied depending on the surrounding environment. Recently, studies using 3D Laser scannerare being conducted to solve these problems. However, terrestrial laser scanners are expensive, so it is difficult to apply at the constructionsite. Therefore, this study proposed a method that can recognize masonry wall defects using a Microelectromechanical systems based LightDetection and Ranging sensor that having much lower price and reliable performance. This study was performed using masonry wallstructures and data were collected from samples having various types of defects in a laboratory environment. Masonry wall defects wererecognized using ResNet-50 and VGG16 models, which are widely used in previous studies. As a result of the classification, ResNet-50 andVGG16 achieved 98.75% and 96.88% accuracy, respectively. The results of this study can be utilized in the development of real-time defectrecognition method for a masonry wall at construction sites.