暗环境下红外目标检测跟踪方法研究

刘辉; 李明益; 韩立金; 刘宝帅

doi:10.12382/bgxb.2024.0081

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暗环境下红外目标检测跟踪方法研究

更新时间：2025-09-25

- 暗环境下红外目标检测跟踪方法研究
- Research on Infrared Target Detection and Tracking in Dark Environments
- 兵工学报 2025年46卷第8期页码：240081
- 作者机构：
  
  1. 北京理工大学机械与车辆学院, 北京 100081
  2. 北京理工大学前沿技术研究院(济南), 山东济南 250300
- 作者简介：
  
  *lj.han@163.com
- 基金信息：
  
  北京市科技新星计划项目(20230484262);教育部集成攻关大平台项目(CX02T01)
- DOI：10.12382/bgxb.2024.0081
  中图分类号：
- 收稿：2024-01-26，
  
  网络出版：2025-08-28，
  
  纸质出版：2025-08-31
- 稿件说明：
移动端阅览
刘辉, 李明益, 韩立金, 等. 暗环境下红外目标检测跟踪方法研究[J]. 兵工学报, 2025,46(8):240081.

Hui LIU, Mingyi LI, Lijin HAN, et al. Research on Infrared Target Detection and Tracking in Dark Environments[J]. Acta Armamentarii, 2025, 46(8): 240081.
刘辉, 李明益, 韩立金, 等. 暗环境下红外目标检测跟踪方法研究[J]. 兵工学报, 2025,46(8):240081. DOI： 10.12382/bgxb.2024.0081.

Hui LIU, Mingyi LI, Lijin HAN, et al. Research on Infrared Target Detection and Tracking in Dark Environments[J]. Acta Armamentarii, 2025, 46(8): 240081. DOI： 10.12382/bgxb.2024.0081.

摘要

针对暗环境动态特征轮廓模糊、盲区遮挡情况

高效准确地检测跟踪动态目标特征

对灾害救援、搜寻跟踪具有实际意义。为实现暗环境下模糊轮廓特征的有效检测跟踪

提出一种时空关联机制的红外目标实时检测深度学习网络(Spatial Local Dynamic You Only Look Once Version 8

SLD-YOLOv8)

设计非局部自适应Non-local模块和空间通道卷积关联模块

对原YOLOv8网络的瓶颈层Bottleneck CSP进行优化。为有效提取深层空间多尺度表征信息

增加用于小目标检测的160×160检测层和动态检测头

较好地提升暗环境中目标跟踪的边界回归性能

并实时有效地推理出目标特征的相对深度位置信息。实验结果表明

改进后的红外目标检测算法对暗环境下的动态特征检测具有较好的鲁棒性和准确性

其平均精度评估指标mAP_0.5和mAP_0.5:0.95比原模型提高了5.6%和4.5%

证明了新算法对暗环境目标跟踪的有效性。

Abstract

The efficient and accurate detection and tracking of dynamic target features in dark environments

where contours are blurred and occlusions are present

hold practical significance for disaster relief

search and tracking operations.To effectively detect and track the blurred contour features in dark environments

an improved real-time infrared target tracking and detection algorithm is proposed.This algorithm

based on the deep learning network (Spatial Local Dynamic You Only Look Once

SLD-YOLOv8)

incorporates a non-local adaptive module and a spatial channel convolution (SCC) correlation module to optimize the Bottleneck CSP of YOLOv8 network for better feature extraction.A dedicated 160×160 detection layer and a dynamic head are introduced for the improved detection of small-scale targets and the enhanced boundary regression capabilities in low-light scenarios

enabling the accurate real-time inference of relative target position.Experimental validation shows that the proposed algorithm has good robustness and accuracy in detecting the dynamic features in dark environments.The average precision evaluation metrics mAP_0.5 and mAP_0.5:0.95 of this model are increased by 5.6% and 4.5%

respectively

compared to the original model

affirming its effectiveness of tracking the targets in dark environments.

关键词

Keywords

references

ZHAO M J , LI W , LI L , et al . Single-frame infrared small-target detection:a survey [J ] . IEEE Geoscience and Remote Sensing Magazine , 2022 , 10 ( 2 ): 87 - 119 .

LI R H , SHEN Y S . YOLOSR-IST:a deep learning method for small target detection in infrared remote sensing images based on super-resolution and YOLO [J ] . Signal Processing , 2023 , 208 : 108962 .

KOU R K , WANG C P , FU Q , et al . Detection model and performance evaluation for the infrared search and tracking system [J ] . Applied Optics , 2023 , 62 ( 2 ): 398 - 410 .

ARKIN E , YADIKAR N , XU X B , et al . A survey:object detection methods from CNN to transformer [J ] . Multimedia Tools and Applications , 2023 , 82 ( 14 ): 21353 - 21383 .

魏居尚 , 綦秀利 , 尹成祥 , 等 . 未知建筑空间中自主搜索任务规划 [J ] . 火力与指挥控制 , 2024 , 49 ( 7 ): 156 - 161 .

WEI J S , QI X L , YIN C X , et al . An autonomous search planning method for UAV in unknown architectural space [J ] . Fire Control and Command Control , 2024 , 49 ( 7 ): 156 - 161 . (in Chinese)

刘建华 , 尹国富 , 黄道杰 , 等 . 基于特征融合的可见光与红外图像目标检测 [J ] . 激光与红外 , 2023 , 53 ( 3 ): 394 - 401 .

LIU J H , YIN G F , HUANG D J , et al . Object detection in visible light and infrared images based on feature fusion [J ] . Laser and Infrared , 2023 , 53 ( 3 ): 394 - 401 . (in Chinese)

王强 , 吴乐天 , 李红 , 等 . 基于双支网络协作的红外弱小目标检测 [J ] . 兵工学报 , 2023 , 44 ( 10 ): 3165 - 3176 . DOI: 10.12382/bgxb.2022.0605 http://doi.org/10.12382/bgxb.2022.0605 红外弱小目标检测在预警系统和导弹制导中具有重要的作用,一直是红外图像处理中颇受关注的研究方向。由于红外弱小目标具有信杂比低、尺寸小、形状结构不明显和纹理弱等特点,现有的通用目标检测和语义分割网络直接应用到红外弱小目标检测效果不佳,为此提出一种基于双支网络协作的红外弱小目标检测网络(DualNet)。将检测任务划分成两个子任务,即降低漏检和降低虚警,进而设计两个不同的网络架构分别处理,并利用加权融合损失函数将两支网络信息整合,使得DualNet能够有效地平衡漏检率和虚警率。在自建数据集上的实验结果表明:DualNet相较于通用性能较好的FCN、DeepLabv3、cGAN以及U-net语义分割网络模型具备更高的准确率和鲁棒性,其在F1-measure指标上提高了8%;在SIRST公开数据集上的检测性能也显著超过了基于深度学习的红外目标检测模型ACM和MDvsFA-cGAN,以及多个经典的非深度学习红外弱小目标检测方法。研究结果表明,所提出的方法能够有效提高红外弱小目标的检测精度。

WANG Q , WU L T , LI H , et al . An infrared small target detection method via dual network collaboration [J ] . Acta Armamentarii , 2023 , 44 ( 10 ): 3165 - 3176 . (in Chinese) DOI: 10.12382/bgxb.2022.0605 http://doi.org/10.12382/bgxb.2022.0605 Infrared small target detection (ISTD) is a heated topic in infrared image processing, and it is intensively applied in early warning systems and missile guidance. ISTD faces significant challenges such as low signal-to-noise ratio (SNR), small size, lack of distinct shape or structure, and weak texture, making it a demanding task. The performance of conventional object detection networks and semantic segmentation networks considerably deteriorates when applied directly to ISTD tasks. To address this issue, this paper proposes a new dual network collaboration-based image semantic segmentation network for ISTD, termed as DualNet. DualNet divides the task into two sub-tasks, namely reducing missed detections and reducing false alarms, with two sub-networks focusing on their respective targets (with cost reduced) by employing a weighted loss function to integrate sub-network information. DualNet effectively balances the miss detection rate and false alarm rate. Experimental results show that DualNet outperforms general neural network models (e.g. FCN, DeepLabv3, cGAN and U-net) on the ISTD task, with an improved F1-measure by 0.08. Furthermore, our model outperforms ACM and MDvsFA-cGAN, two most representative ISTD models based on deep learning, and several non-deep-learning-based ISTD methods.

CHEN Y H , ZHANG G P , MA Y J , et al . Small infrared target detection based on fast adaptive masking and scaling with iterative segmentation [J/OL ] . IEEE Geoscience and Remote Sensing Letters , 2022 , 19 : 1 - 5 .

ZHANG F , LIN S L , XIAO X Y , et al . Global attention network with multiscale feature fusion for infrared small target detection [J ] . Optics & Laser Technology , 2024 , 168 : 110012 .

JING J Q , JIA B , HUANG B Q , et al . YOLO-D:dual-branch infrared distant target detection based on multi-level weighted feature fusion [C ] // Proceedings of International Conference on Neural Information Processing . Singapore : Springer , 2023 : 140 - 151 .

SHEN L Y , LANG B H , SONG Z X . DS-YOLOv8-based object detection method for remote sensing images [J ] . IEEE Access , 2023 , 11 : 125122 - 125137 .

ZHOU J J , ZHANG B H , YUAN X L , et al . YOLO-CIR:the network based on YOLO and ConvNeXt for infrared object detection [J ] . Infrared Physics & Technology , 2023 , 131 : 104703 .

WANG C Y , LIAO H Y M , WU Y H , et al . CSPNet:a new backbone that can enhance learning capability of CNN [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle,WA,US : IEEE Computer Society and CVF , 2020 : 390 - 391 .

ZHANG H , ZU K K , LU J , et al . EPSANet:an efficient pyramid squeeze attention block on convolutional neural network [C ] // Proceedings of the Asian Conference on Computer Vision . Kyoto,Japan : Springer , 2022 : 1161 - 1177 .

LI J F , WEN Y W , HE L H . SCConv:spatial and channel reconstruction convolution for feature redundancy [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver,Canada : IEEE , 2023 : 6153 - 6162 .

MEI Y Q , FAN Y C , ZHOU Y Q . Image super-resolution with non-local sparse attention [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Wshington,D.C.,US : IEEE , 2021 : 3517 - 3526 .

LIU F C , GAO C Q , CHEN F , et al . Infrared small and dim target detection with transformer under complex backgrounds [J ] . IEEE Transactions on Image Processing , 2023 , 32 : 5921 - 5932 .

DAI X Y , CHEN Y P , XIAO B , et al . Dynamic head:unifying object detection heads with attentions [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Wshington,D.C.,US : IEEE , 2021 : 7373 - 7382 .

MEI Y Q , FAN Y C , ZHANG Y L , et al . Pyramid attention network for image restoration [J ] . International Journal of Computer Vision , 2023 , 131 ( 12 ): 3207 - 3225 .

CARION N , MASSA F , SYNNAEVE G , et al . End-to-end object detection with transformers [C ] // Proceedings of European Conference on Computer Vision . Cham,Switzerland : Springer , 2020 : 213 - 229 .

ZHU X Z , SU W J , LU L W , et al . Deformable DETR:deformable transformers for end-to-end object detection:arXiv:2010.04159 [R ] . Ithaca,NY,US : Cornell University , 2020 :2010.04159.

DAI X B , HU J P , ZHANG H M , et al . Multi-task faster R-CNN for night time pedestrian detection and distance estimation [J ] . Infrared Physics & Technology , 2021 , 115 : 103694 .

ZHANG S F , CHI C , YAO Y Q , et al . Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle,WA,US : IEEE , 2020 : 9759 - 9768 .

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