A stereovision-based efficient measurement approach for surface flatness of concrete members

The surface flatness of concrete members is an important quality characteristic of buildings and civil infrastructure. Existing measurement methods such as the straightedge method, F-numbers method, and laser scanning method have various limitations such as inefficiency in engineering practices, low accuracy, and high costs. Therefore, this paper presents a novel stereovision-based system comprising two industrial cameras and one projector alongside optimized algorithms to efficiently measure the surface flatness of concrete member. The proposed system is highlighted by effective feature point extraction and matching algorithms, the innovative introduction of feature point extraction error to system measurement error evaluation, and enriched flatness indicators to comprehensively assess surface flatness of concrete members. The outputs of the system, namely, the contour maps as visual demonstrations and the flatness indicator results, can collectively reflect various roughness conditions with high speed and precision. Experiments at multiple levels have validated the proposed algorithms and measurement system, showing that it is capable for providing fast and accurate measures for the surface flatness of concrete members. Further research and developments are sought in system optimization and expanding its applications to other structural members and engineering practices.