Spatial-temporal evolution of landslide cracks revealed by UAV photogrammetry: The FCS-YOLO model for precise crack extraction under complex backgrounds

Accurately identifying and monitoring loess landslide cracks is crucial for understanding slope failure mechanisms and predicting their evolution. However, the complex background conditions of the Loess Plateau — including variations in light intensity, visual clutter, irregular crack morphologies, and heterogeneous scales — severely limit extraction accuracy and impede the detection of subtle crack evolution. To overcome these limitations, this study proposes FCS-YOLO, a novel, high-precision deep learning model for automatic identification of loess landslide cracks from UAV photogrammetry data. In the proposed model, three elaborate modules — Feature Enhanced Gated Convolutional Attention (FGCA), Cooperative Channel-Spatial-Pixel Attention (CCSP), and Scale-Aware Feature Enhancement and Fusion (SAEF) — are integrated into YOLOv8. These modules significantly enhance crack-specific feature extraction, reducing the overall error rate by ~23.1% relative to the baseline and enabling precise crack delineation. Subsequently, we introduce the primary crack identification method, Hazard-Response Crack Significance Index (HRCSI), a novel metric designed to identify cracks with the highest potential to evolve into slip surfaces. Finally, we resolve two long-standing questions — where cracks are most likely to develop and which crack parameter best correlates with their evolution process — through a coupling analysis of crack dynamics and surface deformation. Our code and data are publicly available at: https://github.com/Zpx517/FCS-YOLO.