RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds
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摘要: 弱小目标检测是红外探测与跟踪任务中的经典难题。针对复杂背景下红外弱小目标普遍存在检测率低、虚警率高的问题,提出一种基于区域双邻域显著图(Regional Bi-Neighborhood Saliency Map,RBNSM)的复杂背景红外弱小目标检测新方法。利用弱小目标的局部先验特性定义滑动窗口并划分为多个单元,计算中心单元前若干个最大灰度的均值来凸显弱目标;分别构建中心单元的相接邻域和相隔邻域并计算各自的灰度均值,进而,从不同方向上提取两邻域显著图并点乘二者以进一步抑制杂波背景、增强弱小目标;最后,通过自适应提取准确检测目标。多种典型红外复杂背景图像和SIRST数据集检测结果表明:与7种代表性方法相比,RBNSM在复杂背景下具有更好的检测性能与杂波抑制能力。Abstract: Infrared dim and small target (IRDST) detection is a longstanding and challenging problem in infrared search and track systems. To address the problems of a low detection rate and high false alarm rate for dim and small targets in complex backgrounds, a method is proposed for detecting IRDSTs using a regional bi-neighborhood saliency map (RBNSM). First, using the local a-priori property of the weak target, a sliding window is defined and divided into multiple cells before the mean value of the first maximum gray levels of the central cell is calculated to highlight the weak target. Then, the adjacent and spaced neighbors of the central cell are constructed and the mean value of their respective gray levels is calculated. Subsequently, the salient maps of the two neighbors are the extracted from different directions and multiplied point by point to further suppress the clutter background and enhance the weak target. Finally, the target is accurately detected by adaptive extraction. The detection results of various typical IR complex background images and SIRST datasets show that RBNSM has a better detection performance and clutter suppression ability in complex backgrounds than the seven representative methods.
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表 1 本方法在SIRST数据集上不同K值与λ值的nIoU值
Table 1. nIoU values for different K and λ values of this method on the SIRST dataset
K 1 2 3 4 5 6 nIoU 0.5472 0.5558 0.5467 0.5366 0.5131 0.4888 K 7 8 9 nIoU 0.4517 0.4186 0.3862 λ 0 1 2 3 4 5 nIoU 0.4846 0.5445 0.5558 0.5533 0.5499 0.5466 表 2 测试图像基本特征
Table 2. Basic features of the test image
Image Size Target Size SCR Scene Description a 126×127 4×3 1.7862 Building edge and heavy noise b 128×128 3×3 1.4275 White patches and heavy noise c 255×320 3×2 1.1152 Faint targets drown in background d 305×405 2×3 0.8040 Strong cloud interference e 256×256 3×3 1.1950 Strong building edge interference 表 3 5种不同场景红外图像的SCRG,BSF和时间消耗
Table 3. SCRG, BSF and time consumption for 5 different scenes of IR images
Image Index AAGD[26] PSTNN[17] LEF[24] TLLCM[25] HBMLCM[22] RLCM[23] RBNSM a SCRG
BSF
Time/s3.4482
2.5841
0.01037.2142
3.2782
0.145421.0433
6.1053
2.876810.7899
3.0621
1.50736.0033
2.9321
0.03838.7069
1.0584
2.358055.8439
10.7154
0.1193b SCRG
BSF
Time/s3.4781
6.3289
0.01333.4120
12.5161
0.10235.5669
4.4671
2.05807.7367
4.3538
1.45253.3712
2.7164
0.01100.8943
1.7737
2.147265.5746
18.5168
0.1476
cSCRG
BSF
Time/s0.0524
10.4722
0.03870.0137
26.0004
0.554629.4364
11.5055
10.275620.0115
11.5709
6.04540.3882
8.9662
0.04341.2931
4.1872
10.7094126.9818
38.0842
0.5666
dSCRG
BSF
Tim/s15.0705
1.1310
0.0537119.5938
32.2470
1.158366.1633
23.6170
18.514136.5679
11.6517
8.548478.5632
19.0847
0.065111.8546
5.7182
15.3649180.5421
183.3723
1.6434e SCRG
BSF
Time/s16.4635
4.6741
0.027628.2547
11.6653
0.417453.0661
18.7079
8.633669.5144
26.1979
5.441431.0343
10.7358
0.025725.8079
4.6734
8.5788217.6813
50.7863
0.4615表 4 每种方法在SIRST数据集上的平均指标
Table 4. Average metrics for each method on the SIRST dataset
AAGD PSTNN LEF TLLCM HBMLCM RLCM ACM[11] RBNSM SCRG 167.795 543.545 120.466 200.517 174.422 97.610 - 964.319 BSF 34.174 73.079 22.717 46.599 61.351 9.235 - 280.170 nIoU 0.2871 0.5987 0.2282 0.1943 0.2639 0.1418 0.4154 0.5558 Time(CPU/s) 0.012 0.138 8.557 4.032 0.026 6.515 1.137 0.438 -
[1] GUAN X, ZHANG L, HUANG S, et al. Infrared small target detection via non-convex tensor rank surrogate joint local contrast energy[J]. Remote Sensing, 2020, 12(9): 1520. doi: 10.3390/rs12091520 [2] LU R, YANG Xiaogang, LI W, et al. Robust infrared small target detection via multidirectional derivative-based weighted contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 1(1): 1-5. [3] ZHANG L, LIN Z. Infrared small target detection based on anisotropic contrast filter[C]//2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020: 70-73. [4] SUN Y, YANG J, 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, 2020(99): 1-16. [5] GAO C, MENG D, 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] 吴双忱, 左峥嵘. 基于深度卷积神经网络的红外小目标检测[J]. 红外与毫米波学报, 2019, 38(3): 371-380. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201903019.htmWU Shuangchen, ZUO Zhengrong. Small target detection in infrared images using deep convolutional neural networks[J]. Journal of Infrared Millimeter Waves, 2019, 38(3): 371-380. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYH201903019.htm [7] 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(99): 1-12. [8] LIN S, HAN Z, LI D, et al. Integrating model-and data-driven methods for synchronous adaptive multi-band image fusion[J]. Information Fusion, 2020, 54: 145-160. doi: 10.1016/j.inffus.2019.07.009 [9] 赵兴科, 李明磊, 张弓, 等. 基于显著图融合的无人机载热红外图像目标检测方法[J/OL]. 自动化学报: 1-15. [2021-07-02]. http://kns.cnki.net/kcms/detail/11.2109.tp.20200421.1108.003.html.ZHAO Xingke, LI Minglei, ZHANG Gong, et al. Object Detection Method Based on Saliency Map Fusion for UAV-borne Thermal Images[J/OL]. Acta Automatica Sinica, 1-15. [2021-07-02]. http://kns.cnki.net/kcms/detail/11.2109.tp.20200421.1108.003.html. [10] WANG H, ZHOU L, WANG L. 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. [11] 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 (WACV). 2021: 950-959. [12] LI B, XIAO C, WANG L, et al. Dense Nested Attention Network for Infrared Small Target Detection[J/OL]. arXiv preprint arXiv: 2 106.00487, 2021. [13] ZHAO M, CHENG L, YANG X, et al. TBC-Net: A real-time detector for infrared small target detection using semantic constraint[J/OL]. Computer Vision and Pattern Recognition, 2019. https://arxiv.org/abs/2001.05852. [14] NIE Y, LI W, ZHAO M, et al. Infrared small target detection in image sequences based on temporal low-rank and sparse decomposition[C]// Proc. SPIE, Twelfth International Conference on Graphics and Image Processing, 2021: 11720A. [15] 薛锡瑞, 黄树彩, 马佳顺, 等. 基于局部熵参考预处理的RPCA红外小目标检测[J]. 红外技术, 2021, 43(7): 649-657. http://hwjs.nvir.cn/article/id/e8541151-1530-4561-ad38-42349b5da1b8XUE Xirui, HUANG Shucai, MA Jiashun, et al. RPCA infrared small target detection based on local entropy reference in preprocessing[J]. Infrared Technology, 2021, 43(7): 649-657. http://hwjs.nvir.cn/article/id/e8541151-1530-4561-ad38-42349b5da1b8 [16] ZHANG T, WU H, LIU Y, et al. Infrared small target detection based on non-convex optimization with lp-norm constraint[J]. Remote Sensing, 2019, 11(5): 559. doi: 10.3390/rs11050559 [17] ZHANG L, PENG Z. Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm[J]. Remote Sensing, 2019, 11(4): 382. doi: 10.3390/rs11040382 [18] ZHANG Tianfang, PENG Zhenming, WU Hao, et al. Infrared small target detection via self-regularized weighted sparse model[J]. Neurocomputing, 2021, 420: 124-148. doi: 10.1016/j.neucom.2020.08.065 [19] CHEN C L P, LI H, WEI Y, et al. A local contrast method for small infrared target detection[C]//IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581. Doi: 10.1109/TGRS.2013.2242477. [20] 刘松涛, 刘振兴, 姜宁. 基于融合显著图和高效子窗口搜索的红外目标分割[J]. 自动化学报, 2018, 44(12): 2210−2221 https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201812008.htmLIU Songtao, LIU Zhenxing, JIANG Ning. Target segmentation of infrared image using fused saliency map and efficient subwindow search[J]. Acta Automatica Sinica, 2018, 44(12): 2210−2221 https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201812008.htm [21] WEI Y T, YOU X G, LI H. Multiscale patch-based contrast measure for small infrared target detection[J]. Pattern Recogn., 2016, 58: 216-226. doi: 10.1016/j.patcog.2016.04.002 [22] SHI Y F, WEI Y T, YAO H, et al. High-boost-based multiscale local contrast measure for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15: 33-37. doi: 10.1109/LGRS.2017.2772030 [23] HAN JH, LIANG K, ZHOU B, et al. Infrared small target detection utilizing the multiscale relative local contrast measure[J]. IEEE Geosci. Remote Sens. Lett., 2018, 15: 612-616. doi: 10.1109/LGRS.2018.2790909 [24] XIA C, LI X, ZHAO L, 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 [25] HAN J, 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 Letter, 2020, 17(10): 1822-1826 doi: 10.1109/LGRS.2019.2954578 [26] DENG H, SUN X, LIU M, et al. Infrared small-target detection using multiscale gray difference weighted image entropy[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 60-72. doi: 10.1109/TAES.2015.140878 [27] HUANG S, LIU Y, HE Y, et al. Structure-adaptive clutter suppression for infrared small target detection: chain-growth filtering[J]. Remote Sensing, 2020, 12(1): 47-69.