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梁杰 , 李磊 , 任君 . 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 . Infrared image occlusion interference detection 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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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 . The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.

ZHANG L D , PENG L B , ZHANG T F , et al . Infrared small target detection via non-convex rank approximation minimization joint l 2,1 norm [J ] . Remote Sensing , 2018 , 10 ( 11 ): 1821 . DOI: 10.3390/rs10111821 http://doi.org/10.3390/rs10111821 http://www.mdpi.com/2072-4292/10/11/1821 http://www.mdpi.com/2072-4292/10/11/1821 To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l1 norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines.

DAI Y M , WU Y Q . Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2017 , 10 ( 8 ): 3752 - 3767 . 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

ZHANG L D , PENG Z M . Infrared small target detection based on partial sum of the tensor nuclear norm [J ] . Remote Sensing , 2019 , 11 ( 4 ): 382 . DOI: 10.3390/rs11040382 http://doi.org/10.3390/rs11040382 http://www.mdpi.com/2072-4292/11/4/382 http://www.mdpi.com/2072-4292/11/4/382 Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

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