GPS拒止环境巡飞器双源地理位置知觉实例分割OA

To address the issues of inability to locate in GPS reject environments for the navigation system of aerial vehicles and the low seg-mentation accuracy caused by blurred edges and mixed backgrounds in aerial imagery,this paper proposes a Dual-source Geo-aware Instance Segmentation Model for ground objects,referred to as DGISM.First,a triple adaptive central moment module along horizontal,vertical,and channel dimensions is designed,along with a parallel path cross-layer interaction structure to aggregate shallow-level details,thereby enhancing instance fea-ture discriminability and overcoming background clutter issues.Second,a multi-core spatial focus module is devel-oped to integrate cross-scale spatial features and deep channel information,improving the model's ability to concen-trate on edge features and addressing the problem of low segmentation accuracy due to edge blur.Lastly,to tackle the problem of localization in GPS reject environments,a dual-source perception and positioning module for aerial im-agery and satellite information is designed.This module extracts segmentation weights to match the feature informa-tion of the aerial vehicle's imagery with a feature memory bank of images containing satellite positioning information,thereby obtaining corresponding geographic location information.Experimental results demonstrate that the proposed dual-source geo-aware instance segmentation model achieves mAP50-95 and mAP50 segmentation accuracies of 69.1％and 88.8％,respectively,with a dual-source feature matching positioning accuracy of 95.6％.Realizing pre-cise geographic location perception and positioning of patrol aircraft by segmenting building targets in GPS reject environments.