基于证据理论的多源图像目标检测方法

doi:10.12305/j.issn.1001-506X.2026.07.01

Abstract

Abstract:

To address the issues of high sample dependence and poor multi-temporal data fusion effectiveness in multi-source image ship target detection, this paper proposes an adaptive decision fusion method based on D-S （Dempster-Shafer） evidence theory. Departing from the traditional fuse-then-detect framework, this method constructs a differentiated basic probability assignment model. It transforms the information interference generated by fusing samples from different time periods into complementary benefits, effectively overcoming the dependence on simultaneous-phase samples in multi-source fusion detection and achieving effective decision fusion of multi-source images. Experimental results show that the proposed method improves average precision （AP） by 0.7% and 2.92% respectively compared to single-source optical and synthetic aperture radar （SAR） image detection. Compared with conventional fusion detection algorithms, it reduces computational overhead while significantly enhancing detection accuracy.

Key words: D-S （Dempster-Shafer） evidence theory, sample dependency, multi-source fusion, target detection

CLC Number:

TP 391.41

Na JIANG, Lin LIU, Shilong CHEN. Multi-source image target detection method based on evidence theory[J]. Systems Engineering and Electronics, 2026, 48(7): 2133-2143.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 1

Fig.10

Table 2

Table 3

References 34

1	SONG H S, WANG L, MI L, et al. DCAPT-Net: a multi-scale ship detection algorithm for SAR images[C]//Proc. of the 10th International Conference on Big Data and Information Analytics, 2024: 441−446.
2	ASHIQUZZAMAN A, OH S, LEE D, et al. Compact deeplearning convolutional neural network based hand gesture classifier application for smart mobile edge computing[C]//Proc. of the International Conference on Artificial Intelligence in Information and Communication, 2020: 119−123.
3	XU S J, JI Y J, WANG G C, et al. GFSPP-YOLO: a light YOLO model based on group fast spatial pyramid pooling[C]//Proc. of the IEEE 11th International Conference on Information, Communication and Networks, 2023: 733−738.
4	WANG H, GAO P. Survey of small object detection methods based on deep learning[C]//Proc. of the 9th International Conference on Intelligent Informatics and Biomedical Sciences, 2024: 221−224.
5	CHEN S L, LIU L, WANG X B, et al. Research on SAR active anti-jamming imaging based on joint random agility of inter-pulse multi-parameters in the presence of active deception[J]. Remote Sensing, 2024, 16 (17): 3303. doi: 10.3390/rs16173303
6	LIU S Z, LI K P, MI J H, et al. Multi-source navigation sensor backend information fusion technology based on D-S evidence theory[C]//Proc. of the 9th International Symposium on System Security, Safety, and Reliability, 2023: 197−202.
7	赵铖鑫. 联合SAR与光学图像的目标检测与识别技术[D]. 成都: 电子科技大学, 2023.
	ZHAO C X. Joint SAR and optical image target detection and recognition technology[D]. Chengdu: University of Electronic Science and Technology of China, 2023.
8	ZHONG Y T, ZHANG S Y, LIU Z Y, et al. Unsupervised fusion of misaligned PAT and MRI images via mutually reinforcing cross-modality image generation and registration[J]. IEEE Trans. on Medical Imaging, 2024, 43 (5): 1702- 1714. doi: 10.1109/TMI.2023.3347511
9	陈稳. 基于光学和SAR遥感影像融合的典型目标检测识别研究[D]. 哈尔滨: 哈尔滨工业大学2019.
	CHEN W. Research on typical target detection and recognition based on optical and SAR remote sensing image fusion[D]. Harbin: Harbin Institute of Technology, 2019.
10	CHEN J L, XU X Q, ZHANG J C, et al. Ship target detection algorithm based on decision-level fusion of visible and SAR images[J]. IEEE Journal on Miniaturization for Air and Space Systems, 2023, 4 (3): 242- 249. doi: 10.1109/JMASS.2023.3269434
11	王梓衡, 沈继锋, 左欣, 等. 基于特征级与决策级融合的农作物叶片病害识别[J]. 江苏大学学报（自然科学版）, 2024, 45 (3): 286- 294.
	WANG Z H, SHEN J F, ZUO X, et al. Crop leaf disease recognition based on feature level and decision level fusion[J]. Journal of Jiangsu University （Natural Science Edition）, 2024, 45 (3): 286- 294.
12	周策, 赵秋博, 王明杰, 等. 基于证据理论的可见光和红外融合检测算法[J]. 无线电工程, 2025, 55 (3): 484- 492.
	ZHOU C, ZHAO Q B, WANG M J, et al. Visible light and infrared fusion detection algorithm based on evidence theory[J]. Radio Engineering, 2025, 55 (3): 484- 492.
13	WANG Z Y, WANG H Q, WANG Y. Pyramid attention network for medical image registration[C]//Proc. of the IEEE International Symposium on Biomedical Imaging, 2024.
14	WU Q, MA Y N, WANG Y, et al. Comparison of pixel-level and feature-level image fusion networks for slow-moving landslide detection[C]//Proc. of the IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2024: 3963−3966.
15	LIU L, SUN N, WANG J C. Spearman-coefficient-based improved D-S evidence theory algorithm considering the initial importance of evidence body and its application on multi-source information fusion[C]//Proc. of the 3rd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, 2023: 312−318.
16	BECK J, KEMETER L M, DÜRRBECK K, et al. Toward integrating ChatGPT into satellite image annotation workflows: a comparison of label quality and costs of human and automated annotators[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, 4366- 4381. doi: 10.1109/JSTARS.2025.3528192
17	HU Y K, TANG Y S, BU S M, et al. Hungarian algorithm integrated with iterative method for multi-UAV penetration game strategy[C]//Proc. of the IEEE International Conference on Unmanned Systems, 2024: 1423−1428.
18	BAKIRCI M, BAYRAKTAR I. The cutting-edge YOLO11 for advanced aircraft detection in synthetic aperture radar (SAR) imagery[C]//Proc. of the 8th International Symposium on Innovative Approaches in Smart Technologies, 2024.
19	DANG Z C, WANG X S. FD-YOLO11: a feature-enhanced deep learning model for steel surface defect detection[J]. IEEE Access, 2025, 13, 63981- 63993. doi: 10.1109/ACCESS.2025.3559733
20	ZHANG B N, CHEN H, CHEN J L, et al. Design and implementation of anti-peeping screen algorithm based on soft-NMS YOLOv7[C]//Proc. of the 7th International Conference on Pattern Recognition and Artificial Intelligence, 2024: 71−76.
21	YUE D S, SHI Y C, CHEN X, et al. Wellhead assignment model and improved hungarian algorithm for large scale cluster well group[C]//Proc. of the 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering, 2021: 296−299.
22	JANOS L L, EDWIN S T, JOSE J A C. Efficient vehicle counting algorithm using gaussian mixing models and IOU based tracker for LPWAN based intelligent traffic management systems[C]// Proc. of the IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, 2022.
23	CHEN C J, LUO B C, YIN K X. Research on bearing fault diagnosis based on DS evidence theory and convolutional neural network[C]//Proc. of the 5th International Seminar on Artificial Intelligence, Networking and Information Technology, 2024: 2360−2364.
24	关爱科, 杨杰. 基于SVM-DS融合算法的消防员效能评估方法[J]. 消防科学与技术, 2024, 43 (12): 1772- 1777.
	GUAN A K, YANG J. Firefighter efficiency evaluation method based on SVM-DS fusion algorithm[J]. Fire Science and Technology, 2024, 43 (12): 1772- 1777.
25	HAI B, YAO H L. An approach to information fusion and group deecision making based on choquet integral operators for interval-valued intuitionistic fuzzy data[C]//Proc. of the International Conference on Machine Learning and Cybernetics, 2018: 109−114.
26	SURATHONG S, MAISEN C, PIYAWONGWISAL P. Modified fuzzy dempster-shafer theory for decision fusion[C]//Proc. of the 13th International Conference on Information Technology and Electrical Engineering, 2021: 244−248.
27	WANG H L, WANG W S, HE J Y. Cafeteria self-service billing system based on YOLOv11 and HSV threshold segmentation algorithm[C]//Proc. of the 5th International Conference on Consumer Electronics and Computer Engineering, 2025: 306−312.
28	GIURGI D V, DEZERT J, JOSSO L, et al. Fusion of semantic segmentation models for vehicle perception tasks[C]//Proc. of the 27th International Conference on Information Fusion, 2024.
29	SUN L F, ZHANG Y Y, FU Z M, et al. An approach to multi-sensor decision fusion based on the improved jousselme evidence distance[C]//Proc. of the International Conference on Control, Automation and Information Sciences, 2018: 189−193.
30	SHERMEYER J, HOGAN D, BROWN J, et al. SpaceNet 6: multi-sensor all weather mapping dataset[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 768−777.
31	CHEN Y S, JIA Z Y, HIROTA K R, et al. A multimodal emotion perception model based on context-aware decision-level fusion[C]//Proc. of the 41st Chinese Control Conference, 2022: 7332−7337.
32	PRABHAKAR K R, SRIKAR V S, BABU R V. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 4724−4732.
33	FU Y, WU X J. A dual-branch network for infrared and visible image fusion[EB/OL]. [2025-04-03]. http://arxiv.org/abs/2101.09643.
34	FU Y, WU X J, KITTLER J. Deep decomposition network for image processing: a case study for visible and infrared image fusion[EB/OL]. [2025-04-03]. http://arxiv.org/abs/2102.10526.

方法	数据集	AP/%	R/%	F1/%
YOLO11-obb	PSRGB	94.93	95.54	90.36
Rotate Faster R-CNN	PSRGB	84.12	88.85	84.16
ROI-Transformer	PSRGB	91.30	93.31	91.28
本文方法	PSRGB	95.63	96.50	95.73
YOLO11-obb	SAR	74.68	86.82	84.85
Rotate Faster R-CNN	SAR	42.44	56.59	45.77
ROI-Transformer	SAR	52.60	62.40	60.64
本文方法	SAR	77.60	87.21	86.71

方法	AP/%	R/%	F1/%
Deepfuse+YOLO11	72.09	85.54	84.22
Dualfuse+YOLO11	71.10	84.34	82.64
Deepdecfuse+YOLO11	78.83	89.40	85.09
WLS+YOLO11	79.69	91.57	86.46
本文方法	87.27	89.51	89.12

方法	FLOPs/G	总参数量/×10⁶	GPU占用率/%	时间/s
Deepfuse+YOLO11	19.43	2.67	11	5.34
Dualfuse+YOLO11	14.87	2.93	29	4.73
Deepdecfuse+YOLO11	28.30	2.95	48	1.41
WLS+YOLO11	6.60	2.65	4	7.88
本文方法	13.20	5.30	9	1.94

[1]	Weishan ZHAO, Siyang YOU, Lijia HUANG, Guangyao ZHOU. Dual-stream SAR aircraft detection network enhanced by scattering topology [J]. Systems Engineering and Electronics, 2026, 48(6): 1848-1858.
[2]	Xin WANG, Shenghua ZHOU, Xinxun ZHANG, Jingyi NAN, Shikun SUN. Full polarization MIMO radar mainlobe suppression jamming suppression method [J]. Systems Engineering and Electronics, 2026, 48(5): 1539-1550.
[3]	Mingyu LU, Fei MENG, Chunmao YE, Zhangfeng LI, Qingyuan ZHAO. A prior knowledge-assisted extended target detection method [J]. Systems Engineering and Electronics, 2026, 48(4): 1209-1217.
[4]	Kun CHENG, Hongtao LEI, Zhixuan LYU. Lightweight unmanned aerial vehicle tracking method of improved YOLOv8 [J]. Systems Engineering and Electronics, 2026, 48(3): 737-750.
[5]	Wei WANG, Yong YANG, Jingwen HAN. Radar sea surface weak target detection technology based on correlation [J]. Systems Engineering and Electronics, 2026, 48(3): 787-794.
[6]	Yanzi MIAO, Zhifei ZHAO, Wei WU. Research on detection method of small sea surface targets based on cost-sensitive learning DBN-XGBoost [J]. Systems Engineering and Electronics, 2026, 48(2): 402-409.
[7]	Kai CHEN, Yongbo ZHAO, Rengli LIU, Haitao DENG, Long SUN. High resolution SAR imaging algorithm for maritime moving ships with airborne long synthetic aperture time [J]. Systems Engineering and Electronics, 2026, 48(2): 456-465.
[8]	Hao DING, Jifeng WEI, Yunlong DONG, Zheng CAO, Hengli YU. Method for detecting small targets using prior information under high sea states [J]. Systems Engineering and Electronics, 2026, 48(1): 44-55.
[9]	Ning GAO, Feng XU, Juan YANG. Parameter estimation algorithm of underwater moving small target in MIMO sonar based on FrFT [J]. Systems Engineering and Electronics, 2025, 47(9): 2797-2807.
[10]	Shuyao LAN, Yu LI, Chunhua ZHANG, Cheng CHI, Chunhui CHEN. Diversity shading vertical array signal transmission strategy for shadow zone detection [J]. Systems Engineering and Electronics, 2025, 47(8): 2454-2462.
[11]	Yingcong LUO, Lei ZHANG, Shaopeng WEI, Zhichao MENG. Joint design algorithm using chaotic waveform and mismatched filter for low sidelobe near mainlobe region [J]. Systems Engineering and Electronics, 2025, 47(8): 2511-2518.
[12]	Weihong FU, Wenhong PENG, Naian LIU. SAR image small target detection method with hybrid attention optimization [J]. Systems Engineering and Electronics, 2025, 47(8): 2519-2526.
[13]	Junhao JIA, Xuebin CHEN, Zhangfeng LI, Chunmao YE. ISAR imaging and detection method for space group target [J]. Systems Engineering and Electronics, 2025, 47(6): 1843-1854.
[14]	Jingwen HAN, Yong YANG, Jing LIAN, Wei WANG. Ship detection method of amplitude-phase polarization feature fusion for radar seeker at high grazing angle [J]. Systems Engineering and Electronics, 2025, 47(4): 1127-1135.
[15]	Houhong XIANG, Hongwei MA, Haijun YU, Yufeng CHEN, Fengyu WANG, Xiaolu ZENG. Constant false alarm rate detection method for self-supervised feature similarity representation [J]. Systems Engineering and Electronics, 2025, 47(12): 4012-4023.

Multi-source image target detection method based on evidence theory

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 34

Related Articles 15

Recommended Articles

Metrics

Comments