GPS拒止环境巡飞器双源地理位置知觉实例分割

doi:10.7527/S1000-6893.2025.32120

Abstract

Abstract:

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 feature discriminability and overcoming background clutter issues. Second， a multi-core spatial focus module is developed to integrate cross-scale spatial features and deep channel information， improving the model’s ability to concentrate 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 imagery and satellite information is designed. This module extracts segmentation weights to match the feature information 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 precise geographic location perception and positioning of patrol aircraft by segmenting building targets in GPS reject environments

Key words: instance segmentation, geolocation perception, dual-source localization, gps reject, central moment perception

CLC Number:

V249

Mian GONG, Yinhui ZHANG, Zifen HE, Guangchen CHEN, Rui ZHANG. Instance segmentation of dual-source geolocation perception for GPS reject environmental aerial vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(2): 332120.

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超参数	数值
批大小（Batch size）	32
初始学习率（Learn rate）	0.01
迭代次数（Epoch）	300
优化器	随机梯度下降（SGD）
Mosic	False

模型	PFIS	TACM	MSFM	mAP50/%	mAP50-95/%	GFLOPs	Size/MB	Inference/ms
Yolov8n-seg				83.8	62.4	12.6	6.75	2.3
+PFIS	√			85.3	65.5	12.9	6.84	2.3
+TACM		√		86.1	66.7	14.7	8.52	2.5
+MSFM			√	85.0	64.6	13.3	7.99	2.1
+PFIS，TACM	√	√		86.9	68.7	14.9	8.64	2.6
DGISM	√	√	√	88.8	69.1	15.4	10.34	2.3

卷积模块	mAP50/%	mAP50-95/%	Infenence/ms
GroupConv	81.3	62.6	2.2
AtrousConv	83.5	62.7	2.5
TransposedConv	84.1	63.7	2.4
AsymConv	85.5	64.8	2.3
DWConv	85.3	65.5	2.1

注意力	mAP50/%	mAP50-95/%	GFLOPs	Inference/ms
SE	83.9	63.1	13.2	2.2
scSE	81.0	59.4	13.0	1.9
GCNet	80.5	60.1	12.9	2.1
PPA	85.8	66.2	19.3	4.4
LFE	86.0	67.6	14.6	3.3
TACM	86.9	68.7	14.9	2.6

特征	C+H	C+W	H+W	mAP50/%	mAP50-95/%	Inference/ms
SFIM	√			82.4	61.8	2.6
SFIM		√		85.9	65.9	2.3
SFIM			√	86.2	64.8	2.8
DFIM	√	√		85.8	66.8	2.6
DFIM	√		√	85.5	68.5	2.8
DFIM		√	√	84.6	63.4	2.7
TFIM	√	√	√	86.9	68.7	2.6

Instance segmentation of dual-source geolocation perception for GPS reject environmental aerial vehicle

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模型	mAP50/%	mAP50-95/%	GFLOPs	Size/MB	Inference/ms
Yolov5n-seg	81.2	61.3	7.1	4.12	7.5
Yolov5s-seg	80.4	60.3	26.4	14.8	11.0
Yolov8n-seg	83.8	62.4	12.6	6.75	2.3
Yolov8s-seg	83.9	66.0	42.8	22.8	4.4
Yolov8m-seg	83.0	64.4	110.5	54.9	6.3
Yolov9c-seg	86.3	69.0	158.5	53.7	8.5
Yolov11n-seg	85.6	66.8	10.5	6.39	2.0
Yolov11s-seg	85.4	65.3	35.7	21.4	2.9
Yolov12n-seg	86.1	63.8	10.4	5.87	2.2
Yolov12s-seg	85.4	63.5	35.4	20.4	4.7
DGISM	88.8	69.1	15.4	10.34	2.3

2292-H1	Label	DGISM/10^-9	Label	Yolov8n-seg/10^-9
定位精度	DGISM 95.6%		Yolov8n-seg 76.1%
TOP1	2292-H1	0.235	2297-H3	2.878
TOP2	2292-H2	0.235	2297-H1	2.878
TOP3	2292-H3	0.235	2297-H2	2.878
TOP4	2304-H3	0.311	2292-H3	2.935
TOP5	2304-H1	0.311	2292-H2	2.935

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主干网络	mAP50/%	mAP50-95/%	GFLOPs	Size/MB	Inference/ms
Yolov8n-seg	83.8	62.4	12.6	6.75	2.3
Resnet50	82.3	64.3	80.2	55.6	7.2
EfficientViT	77.8	50.7	56.7	10.8	4.5
SwinTransformer	73.1	50.2	449.3	60.2	12.2
MobileViT	70.0	51.1	13.8	7.0	5.1
FasterNet	75.9	51.8	57.9	11.3	3.3
Yolov8n-seg+TACM，MSFM	86.6	67.2	15.2	9.75	2.5