Volume 17 Issue 3
May  2024
Turn off MathJax
Article Contents
WANG Bo-xiao, SONG Yan-song, DONG Xiao-na. Indistinguishable points attention-aware network for infrared small object detection[J]. Chinese Optics, 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178
Citation: WANG Bo-xiao, SONG Yan-song, DONG Xiao-na. Indistinguishable points attention-aware network for infrared small object detection[J]. Chinese Optics, 2024, 17(3): 538-547. doi: 10.37188/CO.2023-0178

Indistinguishable points attention-aware network for infrared small object detection

doi: 10.37188/CO.2023-0178
Funds:  Supported by National Key Research and Development Program (No. 2022YFB3902505); Key Project of National Natural Science Foundation of China (No. U2141231); National Natural Science Foundation of China (No. 62305032)
More Information
  • Corresponding author: songyansong2006@126.com
  • Received Date: 11 Oct 2023
  • Rev Recd Date: 30 Oct 2023
  • Accepted Date: 05 Dec 2023
  • Available Online: 16 Jan 2024
  • As aircraft maneuverability increases, multi-frame infrared small target detection methods are becoming insufficient to meet detection requirements. In recent years, significant progress has been achieved in single-frame infrared small-target detection method based on deep learning. However, infrared small targets often lack shape features and have blurred boundaries and backgrounds, obstructing accurate segmentation. According to the problems, an indistinguishable points attention-aware network for infrared small object detection was proposed. First, potential target areas were acquired through a point-based region proposal module while filtering out redundant backgrounds. Then, to achieve high-quality segmentation, the mask boundary refinement module was utilized to identify disordered, non-local indistinguishable points in the coarse mask. Multi-scale features of these difficult points were then fused to perform pixel-wise attention modeling. Finally, A fine segmentation mask was generated through re-predicting the indistinguishable points attention-aware features by point detection head. The mAP of the proposed method reached 87.4 and 63.4 on the publicly available datasets NUDT-SIRST and IRDST, and the F-measure reached 0.8935 and 0.7056, respectively. It can achieve accurate segmentation in multi-detection scenarios and multi-target morphology, suppressing false alarm information while controlling the computational overhead.

     

  • loading
  • [1]
    单秋莎, 谢梅林, 刘朝晖, 等. 制冷型长波红外光学系统设计[J]. 中国光学,2022,15(1):72-78. doi: 10.37188/CO.2021-0116

    SHAN Q SH, XIE M L, LIU ZH H, et al. Design of cooled long-wavelength infrared imaging optical system[J]. Chinese Optics, 2022, 15(1): 72-78. (in Chinese). doi: 10.37188/CO.2021-0116
    [2]
    MA T L, YANG ZH, WANG J Q, et al. Infrared small target detection network with generate label and feature mapping[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 6505405.
    [3]
    SUN Y, YANG J G, AN W. Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 3737-3752. doi: 10.1109/TGRS.2020.3022069
    [4]
    赵鹏鹏, 李庶中, 李迅, 等. 融合视觉显著性和局部熵的红外弱小目标检测[J]. 中国光学,2022,15(2):267-275. doi: 10.37188/CO.2021-0170

    ZHAO P P, LI SH ZH, LI X, et al. Infrared dim small target detection based on visual saliency and local entropy[J]. Chinese Optics, 2022, 15(2): 267-275. (in Chinese). doi: 10.37188/CO.2021-0170
    [5]
    GAO C Q, MENG D Y, YANG Y, et al. Infrared patch-image model for small target detection in a single image[J]. IEEE Transactions on Image Processing, 2013, 22(12): 4996-5009. doi: 10.1109/TIP.2013.2281420
    [6]
    CHEN C L P, LI H, WEI Y T, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581. doi: 10.1109/TGRS.2013.2242477
    [7]
    XIA CH Q, LI X R, ZHAO L Y, et al. Infrared small target detection based on multiscale local contrast measure using local energy factor[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(1): 157-161. doi: 10.1109/LGRS.2019.2914432
    [8]
    HAN J H, MORADI S, FARAMARZI I, et al. A local contrast method for infrared small-target detection utilizing a tri-layer window[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10): 1822-1826. doi: 10.1109/LGRS.2019.2954578
    [9]
    刘彦磊, 李孟喆, 王宣宣. 轻量型YOLOv5s车载红外图像目标检测[J]. 中国光学(中英文),2023,16(5):1045-1055. doi: 10.37188/CO.2022-0254

    LIU Y L, LI M ZH, WANG X X. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023, 16(5): 1045-1055. (in Chinese). doi: 10.37188/CO.2022-0254
    [10]
    PANG Y W, WANG T C, ANWER R M, et al. Efficient featurized image pyramid network for single shot detector[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019: 7328-7336.
    [11]
    YANG X, YAN J CH, FENG Z M, et al. R3Det: Refined single-stage detector with feature refinement for rotating object[C]. Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI Press, 2020.
    [12]
    LIU Y ZH, CAO S, LASANG P, et al. Modular lightweight network for road object detection using a feature fusion approach[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(8): 4716-4728.
    [13]
    ZHANG SH F, WEN L Y, BIAN X, et al. Single-shot refinement neural network for object detection [C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 4203-4212.
    [14]
    YUAN Y, XIONG ZH T, WANG Q. VSSA-NET: Vertical spatial sequence attention network for traffic sign detection[J]. IEEE Transactions on Image Processing, 2019, 28(7): 3423-3434. doi: 10.1109/TIP.2019.2896952
    [15]
    PANG Y W, CAO J L, WANG J, et al. JCS-Net: Joint classification and super-resolution network for small-scale pedestrian detection in surveillance images[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(12): 3322-3331. doi: 10.1109/TIFS.2019.2916592
    [16]
    DAI Y M, WU Y Q, ZHOU F, et al. Attentional local contrast networks for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9813-9824. doi: 10.1109/TGRS.2020.3044958
    [17]
    LI B Y, XIAO CH, WANG L G, et al. Dense nested attention network for infrared small target detection[J]. IEEE Transactions on Image Processing, 2023, 32: 1745-1758. doi: 10.1109/TIP.2022.3199107
    [18]
    WANG K W, DU SH Y, LIU CH X, et al. Interior attention-aware network for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5002013.
    [19]
    GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, 2015: 1440-1448.
    [20]
    DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An Image is worth 16x16 words: Transformers for image recognition at scale[C]. 9th International Conference on Learning Representations, OpenReview. net, 2021.
    [21]
    SUN H, BAI J X, YANG F, et al. Receptive-field and direction induced attention network for infrared dim small target detection with a large-scale dataset IRDST[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-13.
    [22]
    ZHOU X Y, KARPUR A, LUO L J, et al. StarMap for category-agnostic keypoint and viewpoint estimation[C]. Proceedings of the European Conference on Computer Vision, Springer, 2018: 328-345.
    [23]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017: 2999-3007.
    [24]
    YANG Z, LIU SH H, HU H, et al. RepPoints: Point set representation for object detection[C]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, 2019: 9656-9665.
    [25]
    XU B, WANG N Y, CHEN T Q, et al. Empirical evaluation of rectified activations in convolutional network[Z]. Computerence, 2015. DOI: 10.48550/arXiv.1505.00853.
    [26]
    ZHU X ZH, SU W J, LU L W, et al. Deformable DETR: deformable transformers for end-to-end object detection[C]. 9th International Conference on Learning Representations, OpenReview. net, 2020.
    [27]
    WU Y, KIRILLOV A, Massa F, et al. Detectron2[CP/OL]. (2019)[2023-8-24]. https://github.com/facebookresearch/detectron2.
    [28]
    TAN M X, PANG R M, LE Q V. EfficientDet: Scalable and efficient object detection[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020: 10778-10787.
    [29]
    YU F, WANG D Q, SHELHAMER E, et al. Deep layer aggregation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 2403-2412.
    [30]
    LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common Objects in Context[M]. FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision – ECCV 2014. Cham: Springer, 2014: 740-755.
    [31]
    WANG H, ZHOU L P, WANG L. Miss detection vs. false alarm: adversarial learning for small object segmentation in infrared images[C]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, 2019: 8508-8517.
    [32]
    RIVEST J F, FORTIN R. Detection of dim targets in digital infrared imagery by morphological image processing[J]. Optical Engineering, 1996, 35(7): 1886-1893. doi: 10.1117/1.600620
    [33]
    AGHAZIYARATI S, MORADI S, TALEBI H. Small infrared target detection using absolute average difference weighted by cumulative directional derivatives[J]. Infrared Physics & Technology, 2019, 101: 78-87.
    [34]
    HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017: 2980-2988.
    [35]
    KIRILLOV A, WU Y X, HE K M, et al. PointRend: Image segmentation as rendering[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020: 9796-9805.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(7)

    Article views(267) PDF downloads(42) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return