基于Swin Transformer目标全景分割的三峡库首土质滑坡识别OA北大核心CSTPCD

[Objective]Landslide identification is the key to solve the problem of where the geological disaster hazards are in moun-tainous areas.Artificial intelligence,especially deep learning method,began to be widely used in the field of target recognition,but for landslide hazard recognition in complex environments in multi-vegetation mountainous areas,there are problems such as sin-gle model and poor accuracy.[Methods]Therefore,an intelligent recognition method based on Swin Transformer(Shift Windows Transformer)as backbone network combined with panoramic target segmentation is proposed in this paper to identify soil landslide in the head area of the Three Gorges Reservoir.The 485 soil landslides at the head of the Three Gorges Reservoir are made into a sample set and divided into a training set and a test set.The training set is loaded into the Swin Transformer model for training.The model adopts the self-attention mechanism to extract features from the training set and construct feature maps.Finally,this method can achieve effective differentiation between the landslide target and the background area,and then complete the potential hazards identification.At the same time,it is compared with DeepLab V3 model.[Results]The result show that the Swin Trans-former model is higher than the DeepLab V3 model in recognition accuracy and recognition speed,and the accuracy can reach 83.55％in the experiment at the head of the Three Gorges reservoir,and the prediction time of a single image is 0.18 s.[Con-clusion]The result show that the method can rapidly identify soil landslides in the complex environment of multi-vegetation moun-tainous areas,and can provide a reference for landslide hazard investigation of multi-vegetation mountainous areas.