Fixed-Wing UAV Detection Based on Simulated Data Transfer Learning

doi:10.16182/j.issn1004731x.joss.22-0024

Abstract

Abstract:

Data play an important role in visual inspection tasks, but it is difficult to obtain a sufficient amount of real fixed-wing UAV data. Therefore, a data set containing a large number of simulated fixed-wing UAV data and a small number of real fixed-wing UAV data is constructed, and the real fixed-wing UAV data are detected by training simulated fixed-wing UAV data based on the idea of weight transfer. On this basis, a two-stage learning strategy is proposed to further reduce the missed detection rate of UAVs by using multi-scale feature fusion.The simulation results show that simulated data can be used to detect real fixed-wing UAVs, which has potential application prospects in future target detection research.

Key words: visual inspection, fixed-wing UAV, weight transfer, multi-scale feature

CLC Number:

TP391.9

Yu Fu, Yao Zhang, Meng Zhao, Mianzhao Wang, Jiangpeng Zheng, Chen Jia, Shengyong Chen. Fixed-Wing UAV Detection Based on Simulated Data Transfer Learning[J]. Journal of System Simulation, 2023, 35(5): 998-1007.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Table 2

Fig. 5

Fig. 6

Fig. 7

References 39

1	刘丹, 王运宏. 限制型自适应SUSAN边缘检测算法[J]. 计算机辅助设计与图形学学报, 2020, 32(6): 971-978.
	Liu Dan, Wang Yunhong. Constraint Self-Adaptive SUSAN Algorithm for Edge Detection[J]. Journal of Computer-Aided Design and Computer Graphics, 2020, 32(6): 971-978.
2	何炜婷, 曾碧, 陈文轩. 基于轻量级人体姿态估计和图卷积的摔倒实时检测方法[J]. 计算机科学与应用, 2021, 11(4): 783-794.
	He Weiting, Zeng Bi, Chen Wenxuan. Real-Time Fall Detection Based on Light-Weight Human Pose Estimation and Graph Convolution Network[J]. Computer Science and Applications, 2021, 11(4): 783-794.
3	陈淼妙, 续晋华. 基于高分辨率卷积神经网络的场景文本检测模型[J]. 计算机应用与软件, 2020, 37(10): 138-144.
	Chen Miaomiao, Xu Jinhua. Scene Text Detection Model Based on High Resolution Convolutional Neural Networks[J]. Computer Applications and Software, 2020, 37(10): 138-144.
4	邓珍荣, 白善今, 马富欣, 等. 改进YOLO的密集小尺度人脸检测方法[J]. 计算机工程与设计, 2020, 41(3): 874-879.
	Deng Zhenrong, Bai Shanjin, Ma Fuxin, et al. Improved YOLO Dense Small-Scale Face Detection Method [J]. Computer Engineering and Design, 2020, 41(3): 874-879.
5	陈勇, 谢文阳, 刘焕淋, 等. 结合头部和整体信息的多特征融合行人检测[J]. 电子与信息学报, 2022, 44(4): 1453-1460.
	Chen Yong, Xie Wenyang, Liu Huanlin, et al. Multi-feature Fusion Pedestrian Detection Combining Head and Overall Information[J]. Journal of Electronics and Information Technology, 2021, 44(4): 1453-1460.
6	骆子铭, 许书彬, 王杰勋. 一种基于机器学习的TLS恶意流量检测方案[J]. 网络空间安全, 2019, 10(7): 89-94.
	Luo Ziming, Xu Shubin, Wang Jiexun. A Scheme for Identifying Malware Traffic with TLS Data Based on Machine Learning[J]. Cyberspace Security, 2019, 10(7): 89-94.
7	宋羿铭, 解文彬, 周未. 基于机器学习的军事目标检测系统开发研究[J]. 现代计算机, 2020(22): 54-58.
	Song Yiming, Xie Wenbin, Zhou Wei. Development and Research of Military Target Detection System Based on Machine Learning[J]. Modern Computer, 2020(22): 54-58.
8	张富凯, 杨峰, 李策. 基于改进YOLOv3的快速车辆检测方法[J]. 计算机工程与应用, 2019, 55(2): 12-20.
	Zhang Fukai, Yang Feng, Li Ce. Fast Vehicle Detection Method Based on Improved YOLOv3[J]. Computer Engineering and Applications, 2019, 55(2): 12-20.
9	席孝倩, 刘威. 基于目标检测算法的肺结节辅助诊断系统[J]. 计算机与现代化, 2020(11): 1-7.
	Xi Xiaoqian, Liu Wei. Pulmonary Nodule Aided Diagnosis System Based on Target Detection Algorithm[J].Computer and Modernization, 2020(11): 1-7.
10	Cheng X, Shi F, Liu X, et al. A Novel Deep Class-Imbalanced Semisupervised Model for Wind Turbine Blade Icing Detection[J]. IEEE Transactions on Neural Networks and Learning Systems (S2162-237X), 2022, 33(6): 2258-2570.
11	Ahuja S, Panigrahi B K, Dey N, et al. Deep Transfer Learning-Based Automated Detection of COVID-19 from Lung CT Scan Slices[J]. Applied Intelligence (S0924-669X), 2021, 51(1): 571-585.
12	Cheng X, Li G, Han P, et al. Data-Driven Modeling for Transferable Sea State Estimation Between Marine Systems[J]. IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2022, 23(3):2561-2571.
13	Behera S K, Rath A K, Sethy P K. Maturity Status Classification of Papaya Fruits Based on Machine Learning and Transfer Learning Approach[J]. Information Processing in Agriculture (S2214-3173), 2021, 8(2): 244-250.
14	Felzenszwalb P F, Girshick R B, McAllester D, et al. Object Detection with Discriminatively Trained Part-Based Models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2009, 32(9): 1627-1645.
15	He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 770-778.
16	Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]// IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 1-9.
17	Krizhevsky A, Sutskever I, Hinton G E. Imagenet Classification with Deep Convolutional Neural Networks[J]. Advances in Neural Information Processing Systems (S1049-5258), 2012, 25: 1097-1105.
18	Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. Advances in Neural Information Processing Systems (S1049-5258), 2015, 28: 91-99.
19	Girshick R. Fast R-CNN[C]// IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448.
20	Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal Speed and Accuracy of Object Detection[J]. arXiv preprint, arXiv: , 2020.
21	Wang C Y, Liao H Y M, Wu Y H, et al. CSPNet: A New Backbone that can Enhance Learning Capability of CNN[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, WA, USA: IEEE, 2020: 390-391.
22	Lin T Y, Dollár P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 2117-2125.
23	Liu S, Qi L, Qin H, et al. Path Aggregation Network for Instance Segmentation[C]// IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA: IEEE, 2018: 8759-8768.
24	Rezatofighi H, Tsoi N, Gwak J Y, et al. Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Savarese, Silvio. Long Beach, CA, USA: IEEE, 2019: 658-666.
25	宋闯, 赵佳佳, 王康, 等. 面向智能感知的小样本学习研究综述[J]. 航空学报, 2020, 41(增1): 15-28.
	Song Chuang, Zhao Jiajia, Wang Kang, et al. A Survey of Few Shot Learning Based on Intelligent Perception[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S1): 15-28.
26	Dai J, Qi H, Xiong Y, et al. Deformable Convolutional Networks[C]// IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 764-773.
27	Zhang P, Zhong Y, Li X. SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications[C]// IEEE/CVF International Conference on Computer Vision Workshops. Seoul, Korea: IEEE, 2019: 37-45.
28	Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J]. arXiv preprint, arXiv: , 2018.
29	饶威. 基于深度学习的无人机地面小目标算法研究[D]. 沈阳: 沈阳理工大学, 2020.
	Rao Wei. Research on UAV Ground Small Target Algorithm Based on Deep Learning[D]. Shenyang: Shenyang Ligong University, 2020.
30	Zhao Q, Sheng T, Wang Y, et al. M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network[J]. Proceedings of the AAAI Conference on Artificial Intelligence (S2159-5399), 2019, 33: 9259-9266.
31	He K, Zhang X, Ren S, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2015, 37(9): 1904-1916.
32	Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. arXiv preprint, arXiv: , 2014.
33	He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 770-778.
34	Bodla N, Singh B, Chellappa R, et al. Soft-NMS--Improving Object Detection with One Line of Code[C]// IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017: 5561-5569.
35	Chen Q, Wang Y, Yang T, et al. You Only Look One-level Feature[C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE, 2021: 13039-13048.
36	Wong A, Famuori M, Shafiee M J, et al. YOLO Nano: A Highly Compact You Only Look Once Convolutional Neural Network for Object Detection[J]. arXiv preprint, arXiv: , 2019.
37	Long X, Deng K, Wang G, et al. PP-YOLO: An Effective and Efficient Implementation of Object Detector[J]. arXiv preprint, arXiv: , 2020.
38	Carion N, Massa F, Synnaeve G, et al. End-to-End Object Detection with Transformers[C]// 16th European Conference on Computer Vision. Glasgow, UK: Springer, Cham, 2020: 213-229.
39	Ge Z, Liu S, Wang F, et al. YOLOX: Exceeding YOLO Series in 2021[J]. arXiv preprint, arXiv:, 2021.

方法	Precision/%	Recall/%	mAP/%	MR/%	FPS
YOLOF^[35]	100	53.75	77.50	46.25	20.30
YOLO Nano^[36]	40.18	56.25	9.12	43.75	30.08
PP-YOLO^[37]	95.77	85.00	78.20	15.00	72.90
DETR^[38]	98.41	77.5	45.60	22.50	0.52
YOLOX^[39]	92.31	90.00	85.90	10.00	22.98
本文方法	94.05	98.75	91.67	1.25	25.72

方法	Precision/%	Recall/%	mAP/%	MR/%	FPS
YOLOv5	90.67	85.00	72.30	15.00	80.65
M2Det^[30]	74.47	87.50	70.20	12.50	4.65
本文方法	94.05	98.75	91.67	1.25	25.72

[1]	Yandong Liu, Gaoxiang Huang, Wen Chen. Improved Social Force Model Based on Enhancing Psych behavioral Heterogeneity [J]. Journal of System Simulation, 2023, 35(5): 1120-1130.
[2]	Yuxuan Dai, Chenggang Cui. Deep Reinforcement Learning-Based Control Strategy for Boost Converter [J]. Journal of System Simulation, 2023, 35(5): 1109-1119.
[3]	Huicheng Luo, Shujuan Wang. Multi-camera Vehicle Recognition Method Based on Feature Robustness Enhancement [J]. Journal of System Simulation, 2023, 35(5): 1059-1074.
[4]	Yaqian Fan, Songyuan Yu, Fang Fang. Two-Stage Distributed Robust Optimal Dispatching for a Combined Heat and Power Virtual Power Plant [J]. Journal of System Simulation, 2023, 35(5): 1046-1058.
[5]	Junyang Liu, Shisong Zhu. Auxiliary Decision-Making Method for Flight Support Force Allocation Based on DBN [J]. Journal of System Simulation, 2023, 35(5): 1008-1019.
[6]	Yuan Zhang, Haoyu Han, Xie Han, Jiaxu Fu. Point Cloud Registration Method Based on Improved Covariance Matrix Descriptor [J]. Journal of System Simulation, 2023, 35(5): 979-986.
[7]	Chao Fu, Dongyue Wang, Xinxin Li, Min Xue. Simulation Platform for Optimization and Decision Making of Flexible Manufacturing Process of Automatic Production Lines [J]. Journal of System Simulation, 2023, 35(5): 920-935.
[8]	Xiangqian Wang, Puhao Guo, Xiangrui Meng. Simulation and Optimization of Integrated Production Logistics System of Underground Coal Mining, Dressing, and Backfilling [J]. Journal of System Simulation, 2023, 35(5): 907-919.
[9]	Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang. Research Progress of Opponent Modeling Based on Deep Reinforcement Learning [J]. Journal of System Simulation, 2023, 35(4): 671-694.
[10]	Tuo Zhao, Hanqiang Deng, Jialong Gao, Jian Huang. Dynamic Target Assignment of Multiple Unmanned Aerial Vehicles Based on Clustering of Network Nodes [J]. Journal of System Simulation, 2023, 35(4): 695-708.
[11]	Wei Chen, Yan Wang, Zhicheng Ji. Dynamics Modeling and Online Prediction of Energy Consumption of Discrete Manufacturing System [J]. Journal of System Simulation, 2023, 35(4): 760-772.
[12]	Weikai Yang, Yan Wang, Zhicheng Ji. Knowledge Graph-based Process Knowledge Reasoning Method for Intelligent Production System [J]. Journal of System Simulation, 2023, 35(4): 773-785.
[13]	Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei. Multi-agent Cooperative Combat Simulation in Naval Battlefield with Reinforcement Learning [J]. Journal of System Simulation, 2023, 35(4): 786-796.
[14]	Yongzhang Zhou, Yan Wang, Zhicheng Ji. Bottleneck Drift Fluctuation Analysis of Discrete Remanufacturing System under Disturbance [J]. Journal of System Simulation, 2023, 35(4): 809-821.
[15]	Qiang Chen, Yi Wang, Kangshun Li. Research on Modeling and Scheduling of Virtual Power Plant with Dual Demand Response [J]. Journal of System Simulation, 2023, 35(4): 822-832.