Infrared Small Dim Target Detection Based on Local Contrast Mechanism
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摘要: 针对复杂背景和低信杂比条件下的红外弱小目标检测难题,提出了一种基于局部对比度机制的红外弱小目标检测方法。该方法提出了一个包含中心层、中间层和最外层的3层窗口,可以使用单尺度计算完成不同尺度弱小目标的检测。首先,对中心层引入匹配滤波思想,有针对性地增强真实目标;同时,提出最接近滤波原则,对最外层进行背景估计,以缓解目标靠近边缘时的检测难题;然后,在目标增强结果与背景估计结果之间进行比差联合的对比度计算,达到同时增强目标和抑制背景的目的;最后,通过自适应阈值分割,提取真实目标。实验结果表明,相比现有算法而言,该算法可更好地增强目标、抑制复杂背景,且原理简洁易实现,可有效减少运算量。Abstract: A method for infrared (IR) small dim target detection based on a local contrast mechanism is proposed to solve the problem of IR small dim target detection under a complex background and low signal-to-clutter ratio (SCR). A three-layer window consisting of an inner layer, a middle layer, and an outer layer is proposed, so that targets of different scales can be detected using only single-scale calculations. First, the matched filter is applied to the inner layer to enhance the true target purposefully, and the max-close principle is proposed to estimate the background of the outer layer, so that detection becomes easier when the target is near the background edge. Then, the ratio-difference joint local contrast measure is calculated between the enhanced target and the estimated background to enhance the true target and suppress the complex background simultaneously. Finally, an adaptive threshold operation is used to extract the true target. Experimental results show that compared to some existing algorithms, the proposed algorithm can enhance the true target and suppress complex background better, and its principle is simple yet suitable for implementation and can effectively reduce the amount of calculation.
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Key words:
- IR small target /
- target detection /
- local contrast /
- target enhancement /
- background suppression
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表 1 10组红外序列的详细信息
Table 1. Details of the ten IR sequences
Seq Frames Size Target size Target type 1 200 320×240 7×5-4×3 Plane 2 200 320×256 3×3 Plane 3 200 320×256 3×5 Plane 4 70 256×200 4×5-4×10 Plane 5 200 256×320 2×3-3×4 Plane 6 300 256×256 3×3-3×4 Unmanned vehicle 7 300 256×320 2×2-3×3 Vehicle 8 100 256×256 3×5 Vehicle 9 100 256×256 5×5-5×7 Ship 10 200 320×256 3×3 Simulation 表 2 单帧数据库中6幅图像的详细信息
Table 2. Details of the six frames in the single-frame dataset
Signal image Size Target size Target type 1 125×125 4×4 Plane 2 122×122 3×4 Plane 3 125×125 4×5 Plane 4 122×122 3×4 Plane 5 122×122 5×5 Plane 6 122×122 3×4 Unmanned vehicle 表 3 各图像序列下(均以其中一帧为例)不同算法的SCRG值
Table 3. The SCRG values of different algorithms for different image sequences
Seq Target DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed 1 1 6.288 7.287 10.189 15.770 7.644 18.576 51.291 15.022 52.208 125.155 2 1 9.388 17.002 30.240 17.670 9.558 45.192 101.235 24.942 66.480 268.112 3 1 8.301 13.071 18.876 11.862 2.812 46.288 48.935 21.721 121.668 136.551 4 1 1.913 6.096 5.619 2.556 2.926 7.844 12.683 1.113 18.236 30.208 5 1 13.998 46.949 44.121 20.107 8.370 96.634 134.008 33.066 193.366 241.363 6 1 1.145 4.464 5.573 3.839 1.812 6.612 12.713 2.007 14.492 32.199 7 1 0.505 1.940 1.661 1.518 2.066 19.092 3.635 8.343 25.787 11.349 8 1 1.528 2.583 4.453 2.579 2.941 4.266 8.711 1.014 3.535 37.353 9 1 8.432 4.403 0.304 8.141 8.380 38.641 35.368 30.612 62.715 46.386 2 6.086 14.072 7.662 7.071 6.465 34.149 44.157 3.869 3.957 44.898 3 5.950 3.552 2.818 6.591 8.920 19.387 46.395 3.717 6.066 44.086 4 5.331 3.422 0.065 6.591 6.974 18.155 37.250 4.195 7.411 43.760 10 1 1.123 2.023 2.638 7.106 2.680 0.746 0.675 7.374 12.176 378.726 表 4 各图像序列下(均以其中一帧为例)不同算法的BSF值
Table 4. The BSF values of different algorithms for different image sequences
Seq DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed 1 2.124 33.948 1.412 6.139 0.576 681.672 3.423E3 14.658 4.279 5.360E3 2 7.143 94.592 0.110 15.371 0.029 39.766 1.870E5 37.531 0.229 4.903E5 3 3.231 49.586 0.036 11.120 0.020 15.127 1.347E5 30.609 0.120 3.708E5 4 0.740 17.479 1.042 2.774 0.300 112.150 660.786 1.516 0.510 1.560E3 5 5.706 46.369 0.022 15.135 0.019 22.857 3.894E5 31.337 0.145 6.984E5 6 0.450 6.951 0.056 2.203 0.125 37.561 1.865E3 2.024 0.146 4.700E3 7 0.481 9.174 0.034 2.198 0.136 16.595 4.242E3 8.431 0.030 1.318E4 8 0.732 10.465 0.833 2.841 0.566 81.891 316.084 1.102 0.178 1.334E3 9 2.160 29.866 0.658 5.264 0.742 536.948 2.326E3 32.869 4.776 3.008E3 10 0.645 16.492 0.470 4.121 0.404 44.946 690.432 7.160 1.129 6.437E3 表 5 单帧图像库中不同算法的SCRG值
Table 5. The SCRG values of different algorithms for the single-frame image dataset
Frame Target DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed 1 1 3.667 6.671 3.125 2.814 1.366 25.676 0.607 - - 31.489 2 1 1.106 1.881 0.542 1.750 0.765 9.651 0.608 - - 13.409 3 1 4.277 13.183 9.048 5.356 2.257 13.942 2.445 - - 32.365 4 1 1.659 2.775 4.963 1.513 1.047 4.593 0.182 - - 26.436 5 1 2.038 1.431 1.395 2.064 3.500 9.955 0.181 - - 10.884 6 1 0.469 2.195 3.988 2.522 1.281 4.358 0.995 - - 16.672 表 6 单帧图像库中不同算法的BSF值
Table 6. The BSF values of different algorithms for the single-frame image dataset
Frame DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed 1 1.630 5.904 0.065 2.742 0.235 58.382 172.100 - - 2.699E3 2 0.332 2.677 0.034 1.360 0.260 14.742 93.599 - - 1.268E3 3 1.127 11.319 0.142 2.856 0.148 32.569 357.666 - - 3.024E3 4 0.558 9.072 0.318 1.528 0.292 23.423 90.231 - - 1.036E3 5 0.635 4.898 0.082 1.513 0.358 29.906 60.236 - - 536.425 6 0.199 3.619 0.025 1.308 0.118 4.992 268.685 - - 2.743E3 表 7 各种图像序列下不同算法的运行时间
Table 7. Running times of different algorithms for different image sequences
seconds/frame Seq DoG ILCM NLCM RLCM MPCM WLDM MDTDLMS TCF STLCF Proposed 1 0.232 0.083 0.076 1.739 2.205 4.192 19.110 0.361 0.980 0.902 2 0.253 0.076 0.068 1.965 2.246 4.612 22.469 0.247 0.711 0.948 3 0.269 0.088 0.072 2.166 2.500 4.560 22.232 0.230 0.670 0.949 4 0.202 0.084 0.078 1.220 1.518 3.790 14.140 0.443 0.943 0.670 5 0.262 0.101 0.072 2.023 2.217 4.708 20.826 0.225 0.868 0.972 6 0.232 0.096 0.089 1.597 1.908 3.809 18.385 0.469 1.141 0.743 7 0.285 0.088 0.079 2.117 2.430 4.981 21.576 0.234 0.673 0.912 8 0.256 0.102 0.088 1.736 2.083 3.982 20.190 0.538 1.067 0.749 9 0.209 0.073 0.063 1.566 1.780 3.977 18.346 0.208 0.647 0.762 10 0.253 0.078 0.067 2.043 2.418 4.970 21.844 0.182 0.682 0.922 -
[1] CUI Zheng, YANG Jingli, LI Junbao, et al. An infrared small target detection framework based on local contrast method[J]. Measurement, 2016, 91: 405-413. doi: 10.1016/j.measurement.2016.05.071 [2] GAO Jinyan, LIN Zaiping, AN Wei. Infrared small target detection using a temporal variance and spatial patch contrast filter[J]. IEEE Access, 2019, 7: 32217-32226. doi: 10.1109/ACCESS.2019.2903808 [3] BAI Xiangzhi, BI Yanguang. Derivative entropy-based contrast measure for infrared small-target detection [J]. IEEE Trans. Geosci. Remote Sens., 2018, 56(4): 2452-2466. doi: 10.1109/TGRS.2017.2781143 [4] 潘胜达, 张素, 赵明, 等. 基于双层局部对比度的红外弱小目标检测方法[J]. 光子学报, 2020, 49(1): 184-192. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202001021.htmPAN Shengda, ZHANG Su, ZHAO Ming, et al. Infrared small target detection based on double-layer local contrast measure[J]. Acta Photonica Sinica, 2020, 49(1): 184-192. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202001021.htm [5] WANG Xin, LV Guogang, XU Lizhong. Infrared dim target detection based on visual attention[J]. Infrared Physics & Technology, 2012, 55(6): 513-521. http://www.sciencedirect.com/science/article/pii/S1350449512000801 [6] CHEN C L P, LI Hong, WEI Yantao, 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] HAN Jinhui, MA Yong, ZHOU Bo, et al. A robust infrared small target detection algorithm based on human visual system[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2168-2172. doi: 10.1109/LGRS.2014.2323236 [8] QIN Yao, LI Biao. Effective infrared small target detection utilizing a novel local contrast method[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1890-1894. doi: 10.1109/LGRS.2016.2616416 [9] 王晓阳, 彭真明, 张萍, 等. 局部对比度结合区域显著性红外弱小目标检测[J]. 强激光与粒子束, 2015, 27(9): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201509007.htmWANG Xiaoyang, PENG Zhenming, ZHANG Ping, et al. Infrared small dim target detection based on local contrast combined with region saliency [J]. High Power Laser and Particle Beams, 2015, 27(9): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-QJGY201509007.htm [10] WEI Yantao, YOU Xinge, LI Hong. Multiscale patch-based contrast measure for small infrared target detection [J]. Pattern Recognition, 2016, 58: 216-226. doi: 10.1016/j.patcog.2016.04.002 [11] HAN Jinhui, LIANG Kun, ZHOU Bo, et al. Infrared small target detection utilizing the multi-scale relative local contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4): 612-616. doi: 10.1109/LGRS.2018.2790909 [12] DENG He, SUN Xianping, LIU Maili, et al. Entropy-based window selection for detecting dim and small infrared targets[J]. Pattern Recognition, 2017, 61: 66-77. doi: 10.1016/j.patcog.2016.07.036 [13] KIM S, SUN S G, KIM K T. Highly efficient supersonic small infrared target detection using temporal contrast filter[J]. Electronics Letters, 2014, 50: 81-83. doi: 10.1049/el.2013.2109 [14] DENG Lizhen, ZHANG Jieke, ZHU Hu. Infrared moving point target detection using a spatial-temporal filter[J]. Infrared Physics & Technology, 2018, 95: 122-127. http://www.sciencedirect.com/science/article/pii/S1350449518307916 [15] MORADI S, MOALLEM P, SABAHI M F. Scale-space point function based framework to boost infrared target detection algorithms[J]. Infrared Physics & Technology, 2016, 77: 27-34. http://www.sciencedirect.com/science/article/pii/s1350449516300160 [16] HAN Jinhui, LIU Sibang, QIN Gang, et al. A local contrast method combined with adaptive background estimation for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9): 1442-1446. doi: 10.1109/LGRS.2019.2898893 [17] 回丙伟, 宋志勇, 范红旗, 等. 地/空背景下红外图像弱小飞机目标检测跟踪数据集[J/OL]. 中国科学数据, 2020, 5(3): 286-297(DOI: 10.11922/csdata.2019.0074.zh).HUI Bingwei, SONG Zhiyong, FAN Hongqi, et al. A dataset for infrared detection and tracking of dim-small aircraft targets under ground/air background[J/OL]. China Scientific Data, 2020, 5(3): 286-297(DOI: 10.11922/csdata.2019.0074.zh). [18] OTCBVS WS Series Bench, Roland Miezianko, Terravic Research Infrared Database[DB/OL]. http://vcipl-okstate.org/pbvs/bench/index.html.