Concrete crack growth tracked across views and time for safer structures
Cross-View and Cross-Time Tracking of Crack Evolution in Concrete
Computer Vision and Pattern Recognition
Summary
Tracking how cracks grow over time in concrete structures helps keep them safe and plan repairs. The paper introduces a new way to follow cracks as they appear from different camera angles and at different times, making sure the same crack is recognized and new growth is spotted. The authors combine image alignment with crack shape analysis to track cracks in a shared view. They also created a dataset showing different crack growth patterns to test their method. Results show the method reasonably links cracks across views but still faces challenges with tiny early cracks and camera distortions.
What this means in practice
- •For infrastructure maintenance teams: Automatically track crack growth in concrete bridges and buildings across inspections to improve repair planning accuracy.
- •For construction safety inspectors: Use multi-view and time-based crack monitoring to identify critical crack evolution that affects structural integrity.
Authors
Pengru Deng, Jiapeng Yao, Su Wang, Xuhui He, Zhao Wang, Takanori Emaru, Takashi Matsumoto, Xinrun Li
Abstract
Crack propagation provides richer information than static crack observations for evaluating structural conditions of concrete infrastructure and planning maintenance strategies. Unlike static crack detection, which can now be largely automated using computer vision, vision-based crack tracking is a coupled cross-view and cross-time problem: the same physical crack must retain its identity as camera viewpoints change, while newly developed growth must be distinguished from existing crack segments in a unified reference view. Existing segmentation and 3D reconstruction methods can recover crack masks or scene geometry, but they do not preserve crack identity across views or quantify temporal extension. To address this gap, this study formulates crack growth monitoring as a cross-view and cross-time crack-instance tracking task and proposes a geometry-assisted, morphology-aware framework. The framework first uses image-based geometric registration to align observations from different viewpoints into a shared structural reference, then applies morphology-aware instantiation to assign physically interpretable crack identities, and finally performs temporal mapping in the unified view to distinguish newly developed growth from existing crack segments. To enable evaluation, a temporally annotated dataset was established and used, covering three representative cracking scenarios: single dominant crack propagation, mixed-mode branching, and multiple-crack interaction, with pixel-level masks and cross-time instance labels. Experimental results show that the proposed framework can establish loose cross-view identity correspondence under changing viewpoints, while strict spatial overlap and growth localization remain limited by early micro-crack segmentability and projection drift, highlighting directions for future crack-monitoring research.