ZHANG Xingwang, LI Dawei, LIN Suzhen, LU Xiaofei. Infrared Weak Target Detection Method Based on Sparse Attention[J]. Infrared Technology , 2025, 47(3): 342-350.
Citation: ZHANG Xingwang, LI Dawei, LIN Suzhen, LU Xiaofei. Infrared Weak Target Detection Method Based on Sparse Attention[J]. Infrared Technology , 2025, 47(3): 342-350.

Infrared Weak Target Detection Method Based on Sparse Attention

More Information
  • Received Date: March 03, 2024
  • Revised Date: May 26, 2024
  • In this study, a novel weak infrared small target detection network based on sparse attention and multiscale feature fusion is proposed to address the challenges of low pixel occupancy and limited texture features for weak infrared small targets within complex backgrounds, leading to difficulties in feature extraction, low detection rates, and high false alarm rates. The network utilizes the segmentation attention of ResNest to extract features at different scales. A BiFormer attention module is introduced to learn the distant relationships between targets and backgrounds. Furthermore, a fusion module is employed to merge both high- and low-level features, with the final detection results represented as a binary image through a head module. The experimental results demonstrate that the proposed method achieves the best performance in terms of both Intersection over Union (IoU) and F-measure. Compared with the dense nested attention network (DNANet), the proposed method improved the IoU by 3.9% and F-measure by 5.6%. Compared with the attentive bilateral contextual network (ABCNet), the proposed method improved the IoU by 5.8% and F-measure by 10%. Moreover, the proposed approach exhibited robustness and adaptability in effectively detecting small weak infrared targets in diverse, complex backgrounds. This method is applicable to weak infrared small-target detection in complex backgrounds, exhibiting superior performance.

  • [1]
    KOU R, WANG C, PENG Z, et al. Infrared small target segmentation networks: A survey[J]. Pattern Recognition, 2023, 143: 109788. DOI: 10.1016/j.patcog.2023.109788
    [2]
    崔晨辉, 蔺素珍, 李大威, 等. 基于孪生网络和Transformer的红外弱小目标跟踪方法[J]. 计算机应用, 2024, 44(2): 563-571.

    CUI C H, LIN S Z, LI D W, et al. Infrared weak target tracking method based on twin network and transformer[J]. Journal of Computer Applications, 2024, 44(2): 563-571.
    [3]
    蔺素珍, 张海松, 禄晓飞, 等. RBNSM: 一种复杂背景下红外弱小目标检测新方法[J]. 红外技术, 2022, 44(7): 667-675. http://hwjs.nvir.cn/article/id/fb6071f5-08ab-4944-b4a1-9921a68c5948

    LIN S Z, ZHANG H S, LU X F, et al. RBNSM: A new method for infrared dim and small target detection in complex backgrounds[J]. Infrared Technology, 2022, 44(7): 667-675. http://hwjs.nvir.cn/article/id/fb6071f5-08ab-4944-b4a1-9921a68c5948
    [4]
    LIANG X, LIU L, LUO M, et al. Robust infrared small target detection using hough line suppression and rank-hierarchy in complex backgrounds[J]. Infrared Physics & Technology, 2022, 120: 103893.
    [5]
    LIU F, GAO C, 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. DOI: 10.1109/TIP.2023.3326396
    [6]
    DENG L, ZHANG J, XU G, et al. Infrared small target detection via adaptive M-estimator ring top-hat transformation[J]. Pattern Recognition, 2021, 112: 107729.
    [7]
    ZHANG T, PENG Z, WU H, et al. Infrared small target detection via self-regularized weighted sparse model[J]. Neurocomputing, 2021, 420: 124-148.
    [8]
    LU Y, HUANG S, ZHAO W, et al. Sparse representation based infrared small target detection via an online-learned double sparse background dictionary[J]. Infrared Physics & Technology, 2019, 99: 14-27.
    [9]
    CHEN C L P, LI H, WEI Y, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 574-581.
    [10]
    Pustokhina I V, Pustokhin D A, Vaiyapuri T, et al. An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety[J]. Safety Science, 2021, 142: 105356.
    [11]
    XIE S H, ZHANG W Z, CHENG P, et al. YOLOv4 fire and smoke detection model with embedded channel attention[J]. Chinese Journal of Liquid Crystal & Displays, 2021, 36(10): 1445-1453.
    [12]
    DAI Y, WU Y, ZHOU F, et al. Asymmetric contextual modulation for infrared small target detection[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021: 950-959.
    [13]
    DAI Y, WU Y, ZHOU F, et al. Attentional local contrast networks for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9813-9823.
    [14]
    WANG H, ZHOU L, WANG L, et al. Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 8509-8518.
    [15]
    LI B, XIAO C, WANG L, et al. Dense nested attention network for infrared small target detection[J]. IEEE Transactions on Image Processing, 2022, 32: 1745-1758.
    [16]
    CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[J/OL]. arXiv: 2005.12872, https://arxiv.org/abs/2005.12872.
    [17]
    ZHANG F, LIN S, XIAO X, et al. Global attention network with multiscale feature fusion for infrared small target detection[J]. Optics & Laser Technology, 2024, 168: 110012.
    [18]
    HUANG L, DAI S, HUANG T, et al. Infrared small target segmentation with multiscale feature representation[J]. Infrared Physics & Technology, 2021, 116: 103755.
    [19]
    PAN P, WANG H, WANG C, et al. ABC: attention with bilinear correlation for infrared small target detection[J]. arXiv preprint arXiv: 2303.10321, 2023.
    [20]
    ZHANG H, WU C, ZHANG Z, et al. Resnest: split-attention networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 2736-2746.
    [21]
    ZHU L, WANG X, KE Z, et al. BiFormer: vision transformer with bi-level routing attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 10323-10333.
    [22]
    ZHANG M, YUE K, ZHANG J, et al. Exploring feature compensation and cross-level correlation for infrared small target detection[C]// Proceedings of the 30th ACM International Conference on Multimedia, 2022: 1857-1865.
    [23]
    ZHANG T, LI L, CAO S, et al. AGPCNet: Attention-guided pyramid context networks for detecting infrared small target under complex background[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(4): 4250-4261.
  • Related Articles

    [1]LI Minglu, WANG Xiaoxia, HOU Maoxin, YANG Fengbao. An Object Detection Algorithm Based on Infrared-Visible Feature Enhancement and Fusion[J]. Infrared Technology , 2025, 47(3): 385-394.
    [2]JIN Dan, LIU Xiaoguang, SHI Gang, SONG Renping, ZU Mingxia. 3D Point Cloud Registration Method for Substation Robot Patrol Tracks[J]. Infrared Technology , 2023, 45(6): 678-684.
    [3]DING Jian, GAO Qingwei, LU Yixiang, SUN Dong. Infrared and Visible Image Fusion Algorithm Based on the Decomposition of Robust Principal Component Analysis and Latent Low Rank Representation[J]. Infrared Technology , 2022, 44(1): 1-8.
    [4]HAN Tuanjun, YIN Jiwu. Robust Adaptive Updating Strategy for Missile-borne Infrared Object-tracking Algorithm[J]. Infrared Technology , 2018, 40(7): 625-631.
    [5]YAO Zhaoxia, XIE Tao. Robust Small Dim Object CFA Detection Algorithm Based on Local Contrast Measure in Aerial Complex Background[J]. Infrared Technology , 2017, 39(10): 940-945.
    [6]YANG Zhixiong, YU Chunchao, YAN Min, YUAN Xiaochun, ZENG Bangze, SU Yulu. Particle Filter Infrared Target Tracking Algorithm Based on Feature Fusion[J]. Infrared Technology , 2016, 38(3): 211-217.
    [7]LIU Huan, GU Xiaojing, GU Xingsheng. Thermal Image Stitching Based on Robust Feature Matching[J]. Infrared Technology , 2016, 38(1): 10-20.
    [8]ZHANG Shuang-lei, CHEN Fan-sheng, WANG Tao. A Dim Small Target Detection Algorithm Based on Multi-Features Fusion Algorithm[J]. Infrared Technology , 2015, (8): 635-641.
    [9]ZHANG Sheng, LI Yu-feng, YAN Yun-yang, XU Ming-wei, XIONG Ping, TANG Zun-lie. Moving Target Detection Using Fusion of Visual and Thermal Video for Robust Surveillance[J]. Infrared Technology , 2013, (12): 773-779.
    [10]XING Su-xia, ZHANG Jun-jv, CHANG Ben-kang. Image Fusion Method Based on NSCT and Robustness Analysis[J]. Infrared Technology , 2011, 33(1): 45-48,55. DOI: 10.3969/j.issn.1001-8891.2011.01.011

Catalog

    Article views (38) PDF downloads (12) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return