Real-time Detection and Localization Algorithm for Military Vehicles in Drone Aerial Photography

doi:10.12382/bgxb.2024.0518

Abstract

Abstract:

With the goal of addressing the challenges of real-time observation and localization of high-value military vehicles on the ground, a real-time algorithm for the detection and localization of military vehicles in aerial photography is proposed. An Armed_vehicle dataset for the detection of multi-type and multi-scale military vehicles in an actual combat environment in aerial photography is established. The detection accuracy reaches 85.82% and the detection efficiency is higher by introducing a large kernel attention (LKA)module into the lightweight neural network model YOLOX-Tiny and using the SIoU edge regression function. A monocular visual localization algorithm based on the visible light images from the unmanned aerial vehicles (UAVs) is proposed. The average target localization error is 3.69m at a flight altitude of 100 meters. It indicates that the proposed algorithm can accurately obtain the geographical location of ground targets and has good comprehensive performance and application prospects.

Key words: drone aerial photography, target detection, target localization, military vehicle

CLC Number:

TP391.4

YAO Yu, SONG Chunlin, SHAO Jiangqi. Real-time Detection and Localization Algorithm for Military Vehicles in Drone Aerial Photography[J]. Acta Armamentarii, 2024, 45(S1): 354-360.

Figures/Tables 14

References 20

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数据集属性名称	数据集属性描述
数据集名称	航拍军事车辆数据集Armed_vehicle
图像分辨率	608×608
图像数量	1 884
军事车辆数量	3 408
图像类型	RGB、灰度
军事车辆种类	坦克、装甲车、运输车、野战皮卡、炮弹发射车
军事车辆状态	正常、损伤、损毁
作战场景	城市、山地、河岸、荒漠、草原

数据集属性名称	数据集属性描述
数据集名称	航拍军事车辆数据集Armed_vehicle
图像分辨率	608×608
图像数量	1 884
军事车辆数量	3 408
图像类型	RGB、灰度
军事车辆种类	坦克、装甲车、运输车、野战皮卡、炮弹发射车
军事车辆状态	正常、损伤、损毁
作战场景	城市、山地、河岸、荒漠、草原

算法	mAP/%	FPS	Params/10⁶	Weights/MB
SSD	87.31	11	24.7	188
CenterNet	89.67	12	32.7	124
YOLOv3_MobileNetV3	81.39	26	29.1	88.6
YOLOv5-S	82.28	20	7.3	28.1
YOLOX-S	85.66	19	9.0	34.2
YOLOX-Tiny	82.59	21	5.1	19.3
本文算法	85.82	21	5.1	19.3

算法	mAP/%	FPS	Params/10⁶	Weights/MB
SSD	87.31	11	24.7	188
CenterNet	89.67	12	32.7	124
YOLOv3_MobileNetV3	81.39	26	29.1	88.6
YOLOv5-S	82.28	20	7.3	28.1
YOLOX-S	85.66	19	9.0	34.2
YOLOX-Tiny	82.59	21	5.1	19.3
本文算法	85.82	21	5.1	19.3

参数	目标车辆											总和
参数	A	B	C	D	E	F	G	H	I	J	K	总和
验证数目	51	45	27	66	79	25	88	69	39	43	33	565
最大误差/m	7.19	8.65	6.23	6.76	8.60	8.51	9.76	11.75	8.03	10.59	9.06	11.75
平均误差/m	3.25	3.44	3.77	3.55	2.99	2.84	3.88	4.49	4.05	4.32	3.79	3.69