Two major categoriesof target detection algorithms(six in number)were selected based on the landslide database of visual interpretation to construct a corresponding automatic landslide identification model,and Aba Prefecture,Sichuan Province was taken as the study area to conduct a research on auto-matic landslide identification.A landslide dataset used high-resolution satellite imagery containing 3 120 samples.Four one-stage detection algorithms,i.e.YOLOv5(s,m,l,and x),as well as two two-stage detection algorithms,Faster R-CNN(VGG16 and ResNet-50)were employed to build corresponding landslide recognition models.In order to investigate the influence of the sample number on the model recognition accuracy,the total number of sample datasets was divided into 1 000,2 000,and 3 000.The recognition results were evaluated by landslide test samples,which showed that,of the two categories of object detection models for landslide recognition,the one-stage YOLOv5 models were more suitable than the two-stage Faster R-CNN models.The number of samples influenced the performance of the landslide recognition model.In the case of fewer samples,the YOLOv5s model was selected to obtain a higher recognition accuracy,while with the increase in the number of samples the YOLOv5m model could be used to obtain better landslide recognition results.
维普
万方数据
