高阶微分方程启发的红外小目标检测网络

doi:10.16451/j.cnki.issn1003-6059.202309001

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1792 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the fields of infrared detection and infrared tracking, infrared small target detection is widely applied. However, infrared small target detection poses significant challenges. The existing methods for infrared small target detection fail to address complex background issues while losing detailed information during feature extraction. Therefore, an infrared small target detection network inspired by high-order differential equations is proposed. Under the guidance of the interpretable theory, a fourth-order Adams-guided feature fusion module is designed, incorporating adaptive weight factors to effectively fuse multi-scale information from different levels. High-order difference equations are employed to eliminate redundant information through deep learning. The target feature enhancement module utilizes a residual structure composed of convolutions at different scales to suppress background noise and enhance multi-scale features with high information content. Experiments for small target detection on publicly available SIRST dataset show that the proposed network has advantages in the evaluation metrics and visual quality.

Key words： Infrared Small Target Detection High-Order Differential Equation Feature Fusion Feature Enhancement Multi-scale Feature

Received: 24 May 2023

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62272363,62036007,62176195,62061047,U21A20514), Young Elite Scientists Sponsorship Program by CAST(No.2021QNRC001), Key Research and Development Program of Shaanxi Pro-vince (No.2021GY-034), Special Project on Technological Inno-vation and Application Development(No.cstc2020jscx-dxwtB0032), Chongqing Excellent Scientist Project( No.CSTC2021YCJH-BGZXM0339)

Corresponding Authors: GAO Xinbo, Ph.D., professor. His research interests include image content generation and quality evaluation, computer vision and pattern recognition.

About author:: ZHANG Mingjin, Ph.D., professor. Her research interests include computer vision, image processing and video compression.ZANG Fan, master student. Her research interests include computer vision and image processing.YUE Ke, master student. His research interests include computer vision and image processing. XU Jiamin, master student. Her research interests include computer vision, image processing and pattern recognition. LI Yunsong, Ph.D., professor. His research interests include image/video proce-ssing and transmission, computer vision and chip design.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHANG Mingjin
	ZANG Fan
	YUE Ke
	XU Jiamin
	LI Yunsong
	GAO Xinbo

Cite this article:

ZHANG Mingjin,ZANG Fan,YUE Ke等. Infrared Small Target Detection Network Inspired by High-Order Differential Equation[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 767-777.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202309001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I9/767

[1] 杨小冈,卢瑞涛,陈世伟,等.飞行器红外图像目标检测与跟踪技术.北京,科学出版社, 2022.
(YANG X G, LU R T, CHEN S W, et al.Aircraft Infrared Image Target Detection and Tracking Technology. Beijing, China: Science Press, 2022.)
[2] 赵坤,孔祥维.小目标红外图像背景噪声的抑制及方法讨论.光学与光电技术, 2004, 2(2): 9-12.
(ZHAO K, KONG X W.Background Noise Suppression in Small Targets Infrared Images and Its Method Discussion. Optics & Opto-electronic Technology, 2004, 2(2): 9-12.)
[3] CHEN C L P, LI H, WEI Y T, et al. A Local Contrast Method for Small Infrared Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581.
[4] HAN J H, MA Y, ZHOU B, et al. A Robust Infrared Small Target Detection Algorithm Based on Human Visual System. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2168-2172.
[5] WEI Y T, YOU X G, LI H.Multiscale Patch-Based Contrast Mea-sure for Small Infrared Target Detection. Pattern Recognition, 2016, 58: 216-226.
[6] GAO C Q, MENG D Y, YANG Y, et al. Infrared Patch-Image Model for Small Target Detection in a Single Image. IEEE Transactions on Image Processing, 2013, 22(12): 4996-5009.
[7] DAI Y M, WU Y Q, SONG Y, et al. Non-negative Infrared Patch-Image Model: Robust Target-Background Separation via Partial Sum Minimization of Singular Values. Infrared Physics and Technology, 2017, 81: 182-194.
[8] DAI Y M, WU Y Q.Reweighted Infrared Patch-Tensor Model with Both Non-Local and Local Priors for Single-Frame Small Target Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3752-3767.
[9] ZHANG L D, PENG Z M.Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm. Remote Sensing, 2019, 11. DOI: 10.3390/rs11040382.
[10] WANG X Y, PENG Z M, ZHANG P, et al. Infrared Small Target Detection via Nonnegativity-Constrained Variational Mode Decom-position. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1700-1704.
[11] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature Pyramid Networks for Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 936-944.
[12] WANG H, ZHOU L P, WANG L.Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 8508-8517.
[13] DAI Y M, WU Y Q, ZHOU F, et al. Asymmetric Contextual Modu-lation for Infrared Small Target Detection // Proc of the IEEE Winter Conference on Applications of Computer Vision. Washington, USA: IEEE, 2021: 949-958.
[14] DAI Y M, WU Y Q, ZHOU F, et al. Attentional Local Contrast Networks for Infrared Small Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9813-9824.
[15] ZHANG M J, YUE K, ZHANG J, et al. Exploring Feature Compensation and Cross-Level Correlation for Infrared Small Target Detection // Proc of the 30th ACM International Conference on Multimedia. New York, USA: ACM, 2022: 1857-1865.
[16] ZHANG M J, LI B T, WANG T Y, et al. CHFNet: Curvature Half-Level Fusion Network for Single-Frame Infrared Small Target Detection. Remote Sensing, 2023, 15. DOI: 10.3390/rs15066573.
[17] ZHANG M J, ZHANG R, ZHANG J, et al. Dim2Clear Network for Infrared Small Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61. DOI: 10.1109/TGRS.2023.3263848.
[18] WEINAN E.A Proposal on Machine Learning via Dynamical Sys-tems. Communications in Mathematics and Statistics, 2017, 5(1): 1-11.
[19] CHEN R T O, RUBANOVA Y, BETTENCOURT J, et al. Neural Ordinary Differential Equations // Proc of the 32nd International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2018: 6572-6583.
[20] HE K M, ZHANG X Y, REN S Q, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1026-1034.
[21] HUA W Z, DAI Z H, LIU H X, et al. Transformer Quality in Li-near Time // Proc of the 39th International Conference on Machine Learning. San Diego, USA: JMLR, 2022: 9099-9117.
[22] LI X, WANG W H, HU X L, et al. Selective Kernel Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 510-519.