A High-Performance Model for Landslide Geological Hazard Detection, CDCS-YOLO

Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various YOLO models. It employs deformable convolutional modules combined with GhostConv modules to enhance feature extraction for landslide targets. The framework uses a structured IoU loss function to optimize the alignment of actual and predicted frames in a directional sense. Additionally, it introduces the CoordAtt attention mechanism to accelerate model convergence and improve training efficiency. The experimental results demonstrate that the enhanced YOLO model (CDCS-YOLO), incorporating four key enhancement modules (Coordinate Attention, Deformable Convolutional Networks, the C3 Module/CSP Architecture and SIoU Loss), achieved a maximum mAP of 96.6%, an accuracy of 96.1%, and a frame rate of 142.6 FPS. Notably, it performed exceptionally well in soil landslide detection, achieving an average detection accuracy surpassing 90%. Based on the experimental results, we explored a morphological landslide classification method further as well as a multi-source differential monitoring strategy integrating UAV imagery, field surveys, ground-based LiDAR data, rainfall information and deformation indicators. The proposed method outperforms the baseline approach and is a promising solution for detecting landslides and geological hazards in Xinjiang.