Concrete crack detection and quantification using deep learning and structured light

Considering the deterioration of civil infrastructures, the evaluation of structural safety by detecting cracks is becoming increasingly essential. In this paper, the advanced technologies of deep learning and structured light composed of vision and two laser sensors have been applied to detect and quantify cracks on surfaces of concrete structures. The YOLO (You Only Look Once) algorithm has been used for real-time detection, and the sizes of the detected cracks have been calculated based on the positions of the projected laser beams on the structural surface. Since laser beams may not be projected in parallel due to installation or manufacturing errors, the laser alignment correction algorithm with a specially designed jig module and a distance sensor is applied to increase the accuracy of the size measurement. The performance of the algorithm has been verified through simulations and experimental tests, and the results show that the cracks on the structural surfaces can be detected and quantified with high accuracy in real-time.