Landslides pose a considerable threat to the safety of human settlements and urban construction,and therefore the ac-curate identification of landslides has become a central pre-requisite for many research projects in the field of disaster prevention and mitigation.Traditional landslide source identification mainly relies on manual field investigation,which is difficult,dangerous,inef-ficient and has low detection accuracy.In this paper,image acquisition is carried out by UAV tilt photography and then 3D model-ling is carried out by Context Capture in order to obtain the distance from the back wall of the landslide to reach the houses and cars of the inhabitants and to further assess the landslide hazard.Finally,through the application of deep learning-based landslide target detection algorithm to effectively detect the source of landslide objects,deep learning technology can quickly identify landslide haz-ards and image segmentation,extract the landslide occurred in the region of image recognition to further determine the landslide.In this paper,we compare YOLOV8-RCS-OSA,YOLOV3-tiny,YOLOV5n,and finally choose YOLOV8-RCS-OSA for the target de-tection of landslides and surrounding houses and cars.Datong City,Shanxi Province,Yunzhou District,Xiping Village as a research area,through the application of deep learning-based landslide target detection algorithms can effectively detect landslides,houses,cars are more suitable for landslide disaster prevention and control scenarios,to provide theoretical references and data support for the target's accurate identification and positioning.In this paper,by comparing the three training models of YOLOV8-RCS-OSA YOLOV3-tiny,YOLOV5n,we finally choose YOLOV8-RCS-OSA for landslide object source target detection,and through experi-ments,we proved that the mAP@0.5 of YOLOV8-RCS-OSA model for mudslide object source reaches 99.5%,mAP@0.5∶0.95 reaches 65.2%,and the overall detection effect is much better than YOLOv5n as well as YOLOv3-tiny algorithm models.Accurate and rapid determination of landslide information is very important for predicting the likelihood of landslides,reducing the impact of landslide disasters,improving prevention and control,optimising urban planning and protecting the ecological environment.
维普
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