Abstract
Landslides are a common and highly hazardous geological hazard, causing serious damage to human safety, property economy and surface environment worldwide. Starting from a deep learning data source, we use remote sensing images of landslides as the base data and perform multiple data-strength processing. A landslide hazard detection model is proposed. The study proposes a landslide detection model based on the one-stage object detection model YOLOv4 (You Only Look Once) and reconstructs the model framework using phantom convolution module, group convolution and attention mechanism. The model addresses the problems of slow detection speed of two-stage model and low detection accuracy of one-stage detection model, and greatly reduces the number of parameters of the model with high detection accuracy while maintaining the model with high detection speed.
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