Pixel-level concrete crack quantification through super resolution reconstruction and multi-modality fusion

Abstract

Cracks pose severe threats to the integrity of concrete structures and hence timely detection of concrete cracks are essential for assessment and maintenance of built concrete infrastructure. In particular, the accurate quantification of cracks, preferably in pixel-level with assistance of computer vision, has immense value to explicitly assess the in-service concrete structures. However, current approaches fail to capture fine cracks necessary for early-stage damage identification, and lack both accuracy and robustness under challenging environmental conditions. This study introduces a comprehensive concrete crack quantification algorithm based on the integration of super-resolution and multi-modal feature fusion. It incorporates a super-resolution network to recover fine crack details lost due to motion blur, compression artifacts, or low sensor quality, and a multi-modality feature fusion-based segmentation network (SQFormer) designed to improve segmentation accuracy in visually challenging environments. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art approaches, achieving 92.29% F1-score and 90.62% mIoU for crack segmentation, accurately quantifies thin cracks as narrow as 0.25 mm with an error rate of 7.3% The proposed algorithm enhances crack quantification precision while exceptional robustness, provides reliable quantitative metrics for concrete structural assessment.