Abstract
Structural health monitoring (SHM) combined with digital image technology has been widely applied to infrastructure operation management. However, the linear illumination of the tunnel and the various lining diseases limit the quality of lining-crack recognition. In this article, a novel tunnel-lining crack recognition system is established. The system involves three main procedures: image preprocessing and enhancement, feature extraction, and crack characterization. To meet tunnel environmental conditions, mature image enhancement and morphological algorithms are packaged into the system; meanwhile, this paper proposes differentiated noise filtering and an improved segmenting method combining adaptive partitioning, edge detection and threshold method to improve the recognition accuracy. A self-regulating calibration method that uses parallel projection is also applied to crack characterization, achieving real-time size calibration. The results of experiments to compare the effects of the proposed system and field application tests confirm the stability and reliability of the system. A further deviation factor analysis provides reasonable suggestions for system improvement.
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