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UZAIR M , BRINKWORTH R S , FINN A . Bio-inspired video enhancement for small moving target detection [J ] . IEEE Transactions on Image Processing , 2021 , 30 : 1232 - 1244 . DOI: 10.1109/TIP.2020.3043113 http://doi.org/10.1109/TIP.2020.3043113 Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.

张永梅 , 赖裕平 , 马健喆 , 等 . 基于视频的装甲车和飞机检测跟踪及轨迹预测算法 [J ] . 兵工学报 , 2021 , 42 ( 3 ): 545 - 554 . DOI: 10.3969/j.issn.1000-1093.2021.03.010 http://doi.org/10.3969/j.issn.1000-1093.2021.03.010 针对当前视频目标跟踪算法跟踪多目标容易跟丢的问题，以视频中的装甲车、飞机为研究对象，研究一种改进跟踪学习检测（TLD）的视频多目标检测跟踪算法。对于跟丢的目标，利用Kalman滤波算法的预测功能跟踪视频中典型目标的轨迹，并采用Kalman滤波算法跟踪的轨迹来弥补TLD算法丢失的部分，从而获得视频中典型目标的完整轨迹，以提高视频多目标跟踪的准确率。由于现有轨迹预测算法存在准确性较差的局限性，提出一种基于社交长短时记忆（Social-LSTM）网络的视频典型目标轨迹预测算法，将上下文环境信息和多个目标轨迹之间的相互影响关系融入Social-LSTM网络，预测待检测典型目标的轨迹序列。仿真实验结果表明，所提轨迹预测算法优于传统的LSTM算法、隐马尔可夫模型算法以及混合高斯模型算法，有利于提高视频典型目标轨迹预测的准确率。

ZHANG Y M , LAI Y P , MA J Z , et al. A video-based prediction algorithm for armored vehicle andaircraft detection/tracking and trajectory [J ] . Acta Armamentarii , 2021 , 42 ( 3 ): 545 - 554 . (in Chinese)

梁杰 , 李磊 , 任君 , 等 . 基于深度学习的红外图像遮挡干扰检测方法 [J ] . 兵工学报 , 2019 , 40 ( 7 ): 1401 - 1410 . DOI: 10.3969/j.issn.1000-1093.2019.07.009 http://doi.org/10.3969/j.issn.1000-1093.2019.07.009 红外成像体制进行目标探测和识别时，烟幕、云雾等遮挡类干扰会改变目标特征导致目标识别错误。通过对遮挡干扰区域进行定位和类型判断，在识别处理时进行针对性处理可大大降低识别虚警率，提高识别的抗干扰能力。为此，提出一种基于深度学习单通道检测器改进的红外图像厚云、烟幕遮挡干扰检测方法。该方法通过网络多层特征的复用和融合，实现了多尺度预测；利用动态锚框模块改进锚框机制，提高了检测精度；将网络中的卷积层与批归一化层合并，提高了检测速度；引入中心损失函数对分类函数进行优化，提高了网络对遮挡物的分类能力。在网络训练过程中，提出一种红外样本增广方法，对数据量进行有效扩充，解决了红外图像训练样本获取难的问题。实验结果表明，与未改进前的算法相比，在速度基本相同情况下改进的遮挡干扰检测方法检测精度提高3.7％，有效地解决了复杂环境下红外自动目标识别系统抗干扰能力较弱的问题。

LIANG J , LI L , REN J , et al. Infrared image occlusion interferencedetection method based on deep learning [J ] . Acta Armamentarii , 2019 , 40 ( 7 ): 1401 - 1410 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2019.07.009 http://doi.org/10.3969/j.issn.1000-1093.2019.07.009 The occlusion interference of smoke screen and cloud can change the target characteristics and cause the target identification errors when an infrared imaging system detects and identifies a target. The targeted processing during the identification process can greatly reduce the identification false alarm rate and improve the anti-interference ability of identification by performing the positioning and type judgment of occlusion interference area. To this end, an infrared image thick cloud and smoke screen occlusion interference detection method based on improved deep learning single channel detector is proposed. In the proposed method, the multi-scale prediction is realized by multiplexing and merging the multi-layer features of network, and the detection precision is inceased by using the dynamic anchor frame module to improve the anchor frame mechanism. The detection speed is inceased by merging the convolutional layer and the batch normalization layer in the network, and the classification ability of network for the obstruction is improved by introducing the central loss function to optimize the classification function. An infrared sample augmentation method is proposed for network training, which effectively expands the data volume and solves the problem of difficult acquisition of infrared image training samples. The experimental results show that the proposed method is used to improve the detection accuracy by 3.7% at the same speed, which effectively solves the problem of weak anti-interference ability of infrared automatic target recognition system in complex environment. Key

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吴一全 , 罗子娟 . 基于最小二乘支持向量机时域背景预测的红外弱小目标检测 [J ] . 兵工学报 , 2010 , 31 ( 6 ): 678 - 684 . 针对信噪比较低时，如何有效地抑制自然背景对目标检测的影响，提出了一种基于最小二乘支持向量机（LS-SVM）时域背景预测的红外弱小目标检测方法。首先针对前几帧图像中对应同一位置像素点的灰度值序列，利用参数经粒子群优化的最小二乘支持向量机进行函数拟合，并据此预测下一帧图像在该位置处像素点的灰度值；然后将原始图像与预测图像相减得到预测残差图像，利用基于二维Tsallis-Havrda-Charvat熵的阈值选取快速算法进行分割，并根据小目标运动的连续性和轨迹的一致性进一步分离噪声和小目标。文中给出了实验结果及分析，并与现有的检测红外小目标的空域和时域背景预测算法进行了比较。结果表明所提出的算法具有更高的检测概率，明显优于已有的基于背景预测的红外小目标检测算法。

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刘德鹏 , 李正周 , 曾靖杰 , 等 . 基于多尺度局部对比度和多尺度梯度一致性的红外小弱目标检测算法 [J ] . 兵工学报 , 2018 , 39 ( 8 ): 1526 - 1535 . DOI: 10.3969/j.issn.1000-1093.2018.08.009 http://doi.org/10.3969/j.issn.1000-1093.2018.08.009 针对复杂背景和强杂波干扰下红外小弱目标检测虚警率高的问题，提出了一种基于多尺度局部对比度方法与多尺度梯度一致性方法的红外小弱目标检测算法。利用多尺度局部对比度方法对红外图像中红外小弱目标进行增强，利用多尺度梯度一致性方法剔除复杂背景和强杂波干扰造成的虚警。从信噪比（SNR）增益、平均残留背景绝对值、检测率、虚警率及ROC曲线方面将新算法与max-mean算法、max-median算法、top-hat算法、IPI算法及MGDWIE算法进行了对比。实验显示：新算法相较于对比算法具有更高的SNR增益、更低的平均残留背景绝对值、更高的检测率及更低的虚警率。对比结果表明：新算法在复杂背景和强杂波干扰下具有良好的红外小弱目标检测准确性和鲁棒性，有效改善了复杂背景和强杂波干扰下红外小弱目标检测虚警率高的问题。

LIU D P , LI Z Z , ZENG J J , et al. Infrared dim small target detection based on Multi-scale Local Contrast and Multi-scale Gradient coherence [J ] . Acta Armamentarii , 2018 , 39 ( 8 ): 1526 - 1535 . (in Chinese)

HAN J H , LIU C Y , LIU Y C , et al. Infrared small target detection utilizing the enhanced closest-mean background estimation [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2020 , 14 : 645 - 662 . DOI: 10.1109/JSTARS.4609443 http://doi.org/10.1109/JSTARS.4609443 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443

于劲松 , 万九卿 , 高秀林 . 红外图像弱小点目标检测技术研究 [J ] . 兵工学报 , 2008 , 29 ( 12 ): 1518 - 1521 . 针对低信噪比、背景和噪声干扰严重的红外图像中运动点目标的检测问题，提出了一种背景预测和目标轨迹搜索相结合的高效检测算法。该算法首先对序列图像进行高通滤波，然后对滤波后的图像进行背景预测，将原图像与背景预测图像相减获得残差图像，依据图像阀值分离出少量的候选目标点，再将含有候选目标的序列残差图像经过最大合并算法形成组合帧图像，最后利用目标运动的连续性和一致性在组合帧图像中搜索目标运动轨迹。

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WIEDERMANN S D , O'CARROLL D C . Biologically inspired feature detection using cascaded correlations of off and on channels [J ] . Journal of Artificial Intelligence and Soft Computing Research , 2013 , 3 : 5 - 14 . DOI: 10.2478/jaiscr-2014-0001 http://doi.org/10.2478/jaiscr-2014-0001 https://www.sciendo.com/article/10.2478/jaiscr-2014-0001 https://www.sciendo.com/article/10.2478/jaiscr-2014-0001 Flying insects are valuable animal models for elucidating computational processes underlying visual motion detection. For example, optical flow analysis by wide-field motion processing neurons in the insect visual system has been investigated from both behavioral and physiological perspectives [1]. This has resulted in useful computational models with diverse applications [2,3]. In addition, some insects must also extract the movement of their prey or conspecifics from their environment. Such insects have the ability to detect and interact with small moving targets, even amidst a swarm of others [4,5]. We use electrophysiological techniques to record from small target motion detector (STMD) neurons in the insect brain that are likely to subserve these behaviors. Inspired by such recordings, we previously proposed an ‘elementary’ small target motion detector (ESTMD) model that accounts for the spatial and temporal tuning of such neurons and even their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as direction selectivity [9] and response facilitation for objects moving on extended trajectories [10] are not accounted for by this model. We therefore propose here two model variants that cascade an ESTMD model with a traditional motion detection model algorithm, the Hassenstein Reichardt ‘elementary motion detector’ (EMD) [11]. We show that these elaborations maintain the principal attributes of ESTMDs (i.e. spatiotemporal tuning and background clutter rejection) while also capturing the direction selectivity observed in some STMD neurons. By encapsulating the properties of biological STMD neurons we aim to develop computational models that can simulate the remarkable capabilities of insects in target discrimination and pursuit for applications in robotics and artificial vision systems.

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