Computer vision for automated surface evaluation of concrete bridge decks

Structural health monitoring of concrete bridges requires accurate and efficient surface crack detection. Early detection of cracks helps prevent further damage. Safety inspection tests are conducted at regular intervals to assess deterioration. Traditional methods involve detection of cracks by human visual inspection. These methods are costly, inefficient and labor intensive, especially for long-span bridges. This thesis presents the use of computer vision and pattern recognition techniques in assessment of cracks on a concrete bridge surface. Bridge deck images are first collected using high-resolution cameras mounted on a robot. Statistical inference algorithms are then implemented to build an automated crack detection system. The proposed machine learning method reduces manual effort and enables automatic labeling over large bridge deck areas to quantify size and location for future reference or comparisons. A panoramic camera is used for the purpose of context localization. Additionally, we demonstrate image-stitching to obtain a coherent spatial mosaic of the bridge deck.