基于深度学习及模糊层次分析的毁伤评估算法

doi:10.7527/S1000-6893.2023.29503

Abstract

Abstract:

To address the detection and assessment of military target damage， this paper proposes a two-stage damage assessment method based on deep learning and fuzzy analytic hierarchy process. Firstly， in the Yolov5-based dual-stage target detection subsystem with coordinate attention mechanism， a key region extraction mechanism is employed to extract damage components， and a damage component classifier based on the combination of intersection over union， Hungarian linear matching， and decision tree is used for classification and quantification of damage severity. Then， in the damage assessment subsystem based on the triangular fuzzy analytic hierarchy process， multiple damage assessment weight systems and damage tree criteria are designed to comprehensively consider the damage features and categories extracted from the previous stage， achieving online real-time damage assessment for targets. Experimental results show that on various simulated datasets with multiple interference factors， the Yolov5-based dual-stage target detection subsystem with the attention mechanism achieves an average improvement in detection accuracy of over 3.6% when compared to classical target fine-grained recognition algorithms. It demonstrates better performance in target key region extraction， and damage classification and assessment， providing strong support and reference for target damage assessment and military operation decision making.

Key words: deep learning, damage classification, damage assessment, attention mechanism, fuzzy analytic hierarchy process

CLC Number:

Xiangxi KONG, Wenyuan QIN, Piaoyi SU, Yongzhao HUA, Xiwang DONG, Li WANG, Ying SU, Kun LYU. Damage assessment algorithm based on deep learning and fuzzy analytic hierarchy process[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(19): 329503.

Figures/Tables 25

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 1

Judgment matrix scale

标度	含义
$M 1$	2个因素同等重要
$M 3$	因素A比因素B稍微重要
$M 5$	因素A比因素B明显重要
$M 7$	因素A比因素B强烈重要
$M 9$	因素A比因素B绝对重要
$M 2, M 4, M 6, M 8$	重要度处于2个相邻标度之间
倒数	$j$ 与 $i$ 比较的判断， $d j i = 1 d i j$

Table 1

Table 2

Damage severity assessment［10］

毁伤等级V	对象整体毁伤程度
零毁伤 $v 1$	$0,0.06$
轻微毁伤 $v 2$	$0.06,0.24$
中度毁伤 $v 3$	$0.24,0.42$
严重毁伤 $v 4$	$0.42,0.6$
完全毁伤 $v 5$	$0.6,1$

Table 2

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Table 3

Table 4

Fig.18

Table 5

Assessment results of cockpit damage

毁伤部件	毁伤特征 $θ i j$	毁伤等级	置信度
机舱1	0.086 2	零毁伤	0.819 2
机舱2	0.101 5	零毁伤	0.742 3
机舱3	0.174 5	轻微毁伤	0.622 6
机舱4	0.435 0	中度毁伤	0.740 0
机舱5	0.588 3	中度毁伤	0.646 8
机舱6	0.640 3	严重毁伤	0.561 1
机舱7	0.719 3	完全毁伤	0.546 1

Table 5

Fig.19

Table 6

Assessment results of multiple component damages

机翼 $θ i j$	尾翼 $θ i j$	机舱 $θ i j$	毁伤等级	置信度
0.183 4	0.119 5	0.140 2	轻微毁伤	0.594 9
0.256 6	0.067 8	0.177 3	轻微毁伤	0.455 9
0.270 0	0.204 6	0.579 9	中度毁伤	0.304 1
0.368 8	0.693 6	0.340 2	中度毁伤	0.369 6
0.421 1	0.120 0	0.446 7	中度毁伤	0.437 2
0.514 3	0.354 0	0.294 4	中度毁伤	0.502 2
0.351 3	0.306 4	0.470 4	中度毁伤	0.614 8
0.354 4	0.410 2	0.435 7	中度毁伤	0.641 0
0.592 3	0.331 5	0.800 6	重度毁伤	0.409 5
0.471 1	0.746 7	0.503 8	重度毁伤	0.412 4
0.690 4	0.472 0	0.618 1	完全毁伤	0.428 4
0.391 8	0.900 0	0.672 4	完全毁伤	0.643 3

Table 6

References 25

1	祝学军，赵长见，梁卓，等. OODA智能赋能技术发展思考［J］. 航空学报， 2021， 42（4）： 524332.
	ZHU X J， ZHAO C J， LIANG Z， et al. Thoughts on technology development of OODA empowered with AI［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 524332 （in Chinese）.
2	孙智孝，杨晟琦，朴海音，等. 未来智能空战发展综述［J］. 航空学报， 2021， 42（8）： 525799.
	SUN Z X， YANG S Q， PIAO H Y， et al. A survey of air combat artificial intelligence［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（8）： 525799 （in Chinese）.
3	裴扬，宋笔锋，石帅. 飞机作战生存力分析方法研究进展与挑战［J］. 航空学报， 2016， 37（1）： 216-234.
	PEI Y， SONG B F， SHI S. Analysis method of aircraft combat survivability： Progress and challenge［J］. Acta Aeronautica et Astronautica Sinica， 2016， 37（1）： 216-234 （in Chinese）.
4	勾涛. 基于图像分析的毁伤评估系统关键技术研究［D］. 长春：吉林大学， 2019： 1-4.
	GOU T. Research on key technologies of damage assessment system based on image analysis［D］. Changchun： Jilin University， 2019： 1-4. （in Chinese）.
5	HUANG Y， SHAO C S， WU B， et al. State-of-the-art review on Bayesian inference in structural system identification and damage assessment［J］. Advances in Structural Engineering， 2019， 22（6）： 1329-1351.
6	刘彦，裴晓羽，吕中杰，等. 基于层次分析-模糊综合评价法的相控阵雷达毁伤评估［J］. 北京理工大学学报， 2016， 36（10）： 996-1000.
	LIU Y， PEI X Y， LYU Z J， et al. Damage assessment of phased array antenna based on analytic hierarchy process and fuzzy comprehensive assessment［J］. Transactions of Beijing Institute of Technology， 2016， 36（10）： 996-1000 （in Chinese）.
7	周燕，陈中，李为民. 基于BP神经网络的弹炮结合系统作战效能评估［J］. 系统工程与电子技术， 2005， 27（1）： 84-86.
	ZHOU Y，陈中， LI W M. Evaluation of operational effectiveness for anti-aircraft Gun-missile system based on BP neural networks［J］. Systems Engineering and Electronics， 2005， 27（1）： 84-86 （in Chinese）.
8	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 W J， XU Z L， ZHANG B L， et al. Battle damage assessment method based on BN-cloud model［J］. Acta Armamentarii， 2016， 37（11）： 2075-2084 （in Chinese）.
9	陈麒，祝明波，刘旭东. 基于图像的打击效果评估研究综述［J］. 舰船电子工程， 2013， 33（4）： 10-12， 27.
	CHEN Q， ZHU M B， LIU X D. Summarized of battle damage assessment based on image［J］. Ship Electronic Engineering， 2013， 33（4）： 10-12， 27 （in Chinese）.
10	魏鑫，李晓婷. 基于模糊推理的综合毁伤效果评估方法［J］. 智能计算机与应用， 2022， 12（7）： 146-150.
	WEI X， LI X T. Comprehensive damage effectiveness evaluation method based on fuzzy reasoning［J］. Intelligent Computer and Applications， 2022， 12（7）： 146-150 （in Chinese）.
11	李京，杨根源. 基于神经网络的地面目标威胁度分析［J］. 兵工自动化， 2012， 31（3）： 15-18.
	LI J， YANG G Y. Analysis of ground targets threatening level based on neural network［J］. Ordnance Industry Automation， 2012， 31（3）： 15-18 （in Chinese）.
12	KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks［J］. Communications of the ACM， 2017， 60（6）： 84-90.
13	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 Z T， ZHANG L， XIE C Y， et al. Battle damage effect assessment based on improved GA-BP neural network［J］. Fire Control & Command Control， 2021， 46（11）： 43-48 （in Chinese）.
14	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 L S， LI Y B， ZHAO Y L， 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 （in Chinese）.
15	朱亚红，汪民乐，敬斌. 基于BP神经网络的多源桥梁毁伤信息特征层融合［J］. 战术导弹技术， 2012（2）： 116-121.
	ZHU Y H， WANG M L， JING B. The feature-level fusion of the multi-image damage information about the bridge based on the BP neural network［J］. Tactical Missile Technology， 2012（2）： 116-121 （in Chinese）.
16	徐艺博，于清华，王炎娟，等. 基于多源信息融合的巡飞弹对地目标识别与毁伤评估［J］. 系统仿真学报， 2024， 36（2）： 511-521.
	XU Y B， YU Q H， WANG Y J， et al. 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 （in Chinese）.
17	XU J L， ZENG F， LIU W， 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.
18	HOU Q B， ZHOU D Q， FENG J S. Coordinate attention for efficient mobile network design［C］∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021： 13708-13717.
19	ZHU X K， LYU S C， WANG X， et al. TPH-YOLOv5： improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios［C］∥2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. Piscataway： IEEE Press， 2021： 2778-2788.
20	MILLS-TETTEY A， STENT A， DIAS M B. The dynamic Hungarian algorithm for the assignment problem with changing costs： CMU-RI-TR-07-27［R］. Pittsburgh： Carnegie Mellon University， 2007.
21	彭庭睿，刘海鹏，刘彦，等. 基于模糊层次分析法和图像对比毁伤评估法的目标权重计算方法［J］. 兵工学报， 2021， 42（S1）： 173-180.
	PENG T R， LIU H P， LIU Y， et al. Target weight calculation method based on fuzzy analytic hierarchy process and image contrast damage assessment method［J］. Acta Armamentarii， 2021， 42（S1）： 173-180 （in Chinese）.
22	REZATOFIGHI H， TSOI N， GWAK J， et al. Generalized intersection over union： A metric and a loss for bounding box regression［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2019： 658-666.
23	娄文忠，苏子龙，汪金奎，等. 弹载图像探测器广域协同探测算法［J］. 兵工学报， 2021， 42（11）： 2388-2395.
	LOU W Z， SU Z L， WANG J K， et al. Wide-area cooperative detection algorithm of missile-borne image detector［J］. Acta Armamentarii， 2021， 42（11）： 2388-2395 （in Chinese）.
24	HE K M， GKIOXARI G， DOLLÁR P， et al. Mask R-CNN［C］∥2017 IEEE International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2017： 2980-2988.
25	FU J L， ZHENG H L， MEI T. Look closer to see better： recurrent attention convolutional neural network for fine-grained image recognition［C］∥2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2017： 4476-4484.

场景	对象类型	准确率			精确率			召回率			F1分数
场景	对象类型	本文	Mask R-CNN	RA-CNN	本文	Mask R-CNN	RA-CNN	本文	Mask R-CNN	RA-CNN	本文	Mask R-CNN	RA-CNN
可见光	装甲车	0.91	0.89	0.87	0.92	0.90	0.86	0.89	0.83	0.92	0.91	0.87	0.89
红外	装甲车	0.90	0.83	0.89	0.89	0.86	0.87	0.88	0.81	0.91	0.89	0.84	0.89
可见光	战斗机	0.94	0.93	0.90	0.92	0.92	0.91	0.91	0.92	0.86	0.92	0.92	0.88
红外	战斗机	0.93	0.88	0.88	0.87	0.88	0.89	0.85	0.82	0.82	0.86	0.85	0.85
红外	装甲车、战斗机	0.89	0.81	0.83	0.86	0.84	0.81	0.83	0.83	0.84	0.85	0.84	0.82

对象类型	mAP（基线）/%	mAP（本文）/%
装甲车（可见光、红外）	82.6	88.0
战斗机（可见光、红外）	83.9	86.5

[1]	Jianyu XU, Li ZHOU, Zhanxue WANG, Jie SHI, Hao SHI. Calculation method for hypersonic plume infrared radiation based on a fast line-by-line calculation model [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 630778-630778.
[2]	Lingjie MENG, Hongguang LI, Xinjun LI. SAR image simulation method guided by geomorphic category information [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 331003-331003.
[3]	Zhihao ZHAO, Zhaohua YANG, Yun WU, Yuanjin YU. Single-photon counting imaging denoising method based on deep learning in low-light environment [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 630531-630531.
[4]	Yiquan WU, Kang TONG. Research advances on deep learning-based small object detection in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 30848-030848.
[5]	Guixian QU, Dongyang LIU, Xu YANG, Tian QIU, Chuankai LIU, Shuiting DING, Shuzheng YUAN, Kan GUO. Remaining useful life prediction method based on temporal information enhancement of sensors [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(17): 231634-231634.
[6]	Xiaowei JIANG, Yiquan WU. Research progress of UAV aerial image mosaic methods [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(17): 331799-331799.
[7]	Lin CHEN, Xiwen GU, Zhiying CHEN, Zhuo ZHANG, Xiaoliang SUN. High-precision monocular vision pose measurement for large distance span in carrier landing guidance [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 331568-331568.
[8]	Bin SUN, Hang YOU, Wenbo LI, Xiangrui LIU, Jiayi MA. Dual-band payload image fusion and its applications in low-altitude remote sensing [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531343-531343.
[9]	Fanteng MENG, Yong QIN, Jing CUI, Yunpeng WU, Zicheng ZHANG, Shaowei WEI. Unknown risk detection in external environment of railroad using UAV images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531262-531262.
[10]	Weishi CHEN, Hongchuang NIU, Xin WANG, Jian WAN, Xianfeng LU, Jie ZHANG, Qingbin WANG. Review on multi-source detection technologies for birds and drones in airport clearance area [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 31251-031251.
[11]	Jie LIN, Zhigong TANG, Weiqi QIAN, Yueqing WANG, Peng ZHANG, Weixia XU, Jie LIU. Research progress and prospects of aircraft aerodynamic design based on generative models [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631679-631679.
[12]	Yonghai WANG, Haoge LI, Jiaxin LI, Yi DUAN, Chuan TIAN, Lingxi GUO, Xusheng WU. Rapid aircraft shape generation based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631614-631614.
[13]	Jiaqi LIU, Rongqian CHEN, Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529828-529828.
[14]	Chuanyun WANG, Yang SU, Linlin WANG, Tian WANG, Jingjing WANG, Qian GAO. Multi-object continuous robust tracking algorithm for anti-UAV swarm [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(7): 329017-329017.
[15]	Xudong LUO, Yiquan WU, Jinlin CHEN. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 28822-028822.

Damage assessment algorithm based on deep learning and fuzzy analytic hierarchy process

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 25

References 25

Related Articles 15

Recommended Articles

Metrics

Comments