Addressing Data Scarcity in Crack Detection via CrackModel: A Novel Dataset Synthesis Approach

The application of deep learning in crack detection has become a research hotspot in Structural Health Monitoring (SHM). However, the potential of detection models is often limited due to the lack of large-scale training data, and this issue is particularly prominent in the crack detection of ancient wooden buildings in China. To address this challenge, “CrackModel”, an innovative dataset construction model, is proposed in this paper. This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. To evaluate the effectiveness of synthetic data, systematic experiments were conducted using YOLO-based detection models on both synthetic images and real data. The results demonstrate that synthetic images can effectively simulate real data, providing potential data support for subsequent crack detection tasks. Additionally, these findings validate the efficacy of CrackModel in generating synthetic data. CrackModel, supported by limited baseline data, is capable of constructing crack datasets across various scenarios and simulating future damage, showcasing its broad application potential in the field of structural engineering.