Ground Target Recognition and Damage Assessment of Patrol Missiles Based on Multi-source Information Fusion

doi:10.16182/j.issn1004731x.joss.22-1130

Abstract

Abstract:

For the multiple patrol missiles to attack the high defense capacity targets, a mobile ground target detection and damage assessment method based on multi-source information fusion is proposed. The multi-source information fusion of infrared images and RGB images is carried out by using IoU determination. A novel two-stage tightly coupled damage assessment method based on YOLO-VGGNet of patrol missiles to mobile ground targets is proposed. This method can fully use the advantage of deep semantic information extraction of CNNs and introduce the infrared damaging information simultaneously to achieve the online and real-time damage assessment of mobile ground targets. The results of simulation experiments show that the target recognition algorithm based on multi-source information fusion significantly improves the detection of mobile ground targets of patrol missiles. Compared with the traditional image-change-detection-based method and the two-CNN learning-based method, the real-time and online damage level assessment method based on YOLO-VGGNet improves the accuracy by 19% and 10.3%, respectively.

Key words: multi-source information fusion, damage assessment, convolutional neural networks, YOLO-VGGNet, online real-time assessment

CLC Number:

TP242.6

Xu Yibo, Yu Qinghua, Wang Yanjuan, Guo Ce, Feng Shiru, Lu Huimin. Ground Target Recognition and Damage Assessment of Patrol Missiles Based on Multi-source Information Fusion[J]. Journal of System Simulation, 2024, 36(2): 511-521.

Figures/Tables 11

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References 23

1	吴森堂. 导弹自主编队协同制导控制技术[M]. 北京: 国防工业出版社, 2015.
	Wu Sentang. Cooperative Guidance & Control of Missiles Autonomous Formation[M]. Beijing: National Defense Industry Press, 2015.
2	高昂, 董志明, 叶红兵, 等. 基于深度强化学习的巡飞弹突防控制决策[J]. 兵工学报, 2021, 42(5): 1101-1110.
	Gao Ang, Dong Zhiming, Ye Hongbing, et al. Loitering Munition Penetration Control Decision Based on Deep Reinforcement Learning[J]. Acta Armamentarii, 2021, 42(5): 1101-1110.
3	程进, 罗世彬, 宋闯, 等. 弹群协同与自主决策[M]. 北京: 科学出版社, 2020.
	Cheng Jin, Luo Shibin, Song Chuang, et al. Missile-group Coordination and Autonomous Decision-making[M]. Beijing: Science Press, 2020.
4	曲婉嘉, 徐忠林, 张柏林, 等. 基于贝叶斯网络云模型的目标毁伤评估方法[J]. 兵工学报, 2016, 37(11): 2075-2084.
	Qu Wanjia, Xu Zhonglin, Zhang Bolin, et al. Battle Damage Assessment Method Based on BN-cloud Model[J]. Acta Armamentarii, 2016, 37(11): 2075-2084.
5	Mujica L E, Rodellar J, Fernández A, et al. Q-statistic and T2-statistic PCA-based Measures for Damage Assessment in Structures[J]. Structural Health Monitoring, 2011, 10(5): 539-553.
6	杨青青, 樊桂花. 基于改进模糊综合评判法的建筑物毁伤效果评估[J]. 系统工程与电子技术, 2018, 40(9): 2026-2031.
	Yang Qingqing, Fan Guihua. Battle Damage Assessment of Buildings Based on Improved Fuzzy Comprehensive Evaluation Method[J]. Systems Engineering and Electronics, 2018, 40(9): 2026-2031.
7	田福平, 汶博, 郑鹏鹏. 基于贝叶斯网络的作战目标评估[J]. 火力与指挥控制, 2017, 42(2): 79-82.
	Tian Fuping, Wen Bo, Zheng Pengpeng. Warfare Targets Assessment Based on Bayesian Network[J]. Fire Control & Command Control, 2017, 42(2): 79-82.
8	马晓明, 丁平, 晏卫东. 基于贝叶斯网络的舰船目标毁伤评估[J]. 兵工自动化, 2016, 35(6): 72-75.
	Ma Xiaoming, Ding Ping, Yan Weidong. Warship-damage Assessment Based on Bayesian Networks[J]. Ordnance Industry Automation, 2016, 35(6): 72-75.
9	杨凯达. 加入毁伤时间流的目标毁伤效果评估方法[J]. 舰船电子工程, 2022, 42(4): 129-134, 170.
	Yang Kaida. Method of Battle Damage Assessment with Damage Time Stream[J]. Ship Electronic Engineering, 2022, 42(4): 129-134, 170.
10	Kuncheva L I, Faithfull W J. PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(1): 69-80.
11	Li Qiang, Gong Lixia, Zhang Jingfa. A Correlation Change Detection Method Integrating PCA and Multi- texture Features of SAR Image for Building Damage Detection[J]. European Journal of Remote Sensing, 2019, 52(1): 435-447.
12	Wu Chen, Chen Hong ruiuan, Du Bo, et al. Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network[J]. IEEE Transactions on Cybernetics, 2022, 52(11): 12084-12098.
13	Yousif Osama, Ban Yifang. Improving Urban Change Detection from Multitemporal SAR Images Using PCA-NLM[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2032-2041.
14	涂震飚, 林涛. 基于下视图像的打击效果评估系统研究[J]. 战术导弹技术, 2012(5): 6-9.
	Tu Zhenbiao, Lin Tao. Research on Battle Damage Assessment System Based on Downward-looking Scene Image[J]. Tactical Missile Technology, 2012(5): 6-9.
15	Anastasopoulos I, Anastasopoulos P C, Agalianos A, et al. Simple Method for Real-time Seismic Damage Assessment of Bridges[J]. Soil Dynamics and Earthquake Engineering, 2015, 78: 201-212.
16	He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2016: 770-778.
17	余丽山, 李彦彬, 赵永龙, 等. 基于BP神经网络的飞机抗毁伤能力评估[J]. 弹箭与制导学报, 2018, 38(1): 23-26.
	Yu Lishan, Li Yanbin, Zhao Yonglong, et al. Assessment of Aircraft Anti Damage Capability Based on BP Neural Network[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2018, 38(1): 23-26.
18	张宗腾, 张琳, 谢春燕, 等. 基于改进GA-BP神经网络的目标毁伤效果评估[J]. 火力与指挥控制, 2021, 46(11): 43-48.
	Zhang Zongteng, Zhang Lin, Xie Chunyan, et al. Battle Damage Effect Assessment Based on Improved GA-BP Neural Network[J]. Fire Control & Command Control, 2021, 46(11): 43-48.
19	Shen Yu, Zhu Sijie, Yang Taojianan, et al. BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
20	Sarath P, Soorya M, Shaik Abdul Rahman A, et al. Assessing Car Damage Using Mask R-CNN[J]. International Journal of Engineering and Advanced Technology, 2020, 9(3): 2287-2290.
21	Alqahtani H, Ray A. Neural Network-based Automated Assessment of Fatigue Damage in Mechanical Structures[J]. Machines, 2020, 8(4): 85.
22	Xu Jinglin, Zeng Feng, Liu Wen, et al. Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning[J]. Applied Sciences, 2022, 12(10): 4912.
23	Tang Shimin, Chen Zhiqiang. Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning[J]. Applied Sciences, 2021, 11(9): 3952.

等级	YOLO-VGGNet	PCA-k-means	Mask-RCNN	真值
准确率	84.50	65.50	74.25
正常	86	62	82	100
轻度	79	55	63	100
中度	81	59	68	100
重度	92	86	84	100

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