Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures

Current inspection standards require an inspector to travel to a target structure site and visually assess the structure's condition. This approach is labor-intensive, yet highly qualitative. A less time-consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently, and perform autonomous damage detection. In this paper, a vision-based crack detection methodology is introduced. The proposed approach processes 2D digital images (image processing) by considering the geometry of the scene (computer vision). The crack segmentation parameters are adjusted automatically based on depth parameters. The depth perception is obtained using 3D scene reconstruction. This system extracts the whole crack from its background, where the regular edge-based approaches just segment the crack edges. This characteristic is appropriate for the development of a crack thickness quantification system. Experimental tests have been carried out to evaluate the performance of the proposed system.

► A vision-based crack detection methodology is introduced and evaluated. ► The proposed approach utilizes depth perception to detect and segment cracks. ► The segmentation parameters are adjusted automatically based on depth parameters. ► The depth perception is obtained using 3D scene reconstruction.