吴青青 , 许廷发 , 闫辉 , 等 . 复杂背景下的颜色分离背景差分目标检测方法 [J ] . 兵工学报 , 2013 , 34 ( 4 ): 501 - 506 . DOI: 10. 3969/ j. issn. 1000-1093. 2013. 04. 019 http://doi.org/10. 3969/ j. issn. 1000-1093. 2013. 04. 019 针对复杂背景下运动目标检测失检率高的问题,提出了用于复杂背景目标检测改进的基于RGB 颜色分离的背景差分目标检测方法。主要是对RGB 三通道图像独立进行背景差分运算,阈值二值化后合并三通道前景图像,得到完整前景目标图像;再利用检测的边缘对前景图像进行修正,消除光照变化带来的噪声;RGB 三通道使用自适应权值的递推算法进行背景更新。最后采用实验室采集的图像序列进行了仿真实验,结果表明,该方法在复杂场景下有效识别颜色差异,避免了灰度值相近而造成的目标缺失,提高了检测精确性。

WU Q Q , XU T F , YAN H , et al . An improved color separation method for object detection in complex background [J ] . Acta Armamentarii , 2013 , 34 ( 4 ): 501 - 506 . (in Chinese)

马月红 , 孔梦瑶 . 基于改进快速区域卷积网络的目标检测轻量化算法 [J ] . 兵工学报 , 2021 , 42 ( 12 ): 2664 - 2674 . DOI: 10.3969/j.issn.1000-1093.2021.12.014 http://doi.org/10.3969/j.issn.1000-1093.2021.12.014 基于深度学习的目标检测算法已成为合成孔径雷达（SAR）图像目标检测任务的主流。深层网络通常具有大量参数，运行速度不能满足实时要求，难以在资源受限的设备(如移动端)上部署。考虑到对模型实时性和可移植性的要求，对双阶段目标检测算法快速区域卷积神经网络进行轻量化改进，比较不同改进方法对算法速度与精度的影响。结合SAR图像的特点，优化轻量化模型，与单阶段目标检测算法的单脉冲多盒检测网络对比。仿真实验结果表明，改进轻量化模型在保持原有精度水平下，模型占用内存和算法运算量大大减少，可有效满足SAR图像目标检测的实时性要求。

MA Y H , KONG M Y . A lightweight target detection algorithm based on the improved faster-RCNN [J ] . Acta Armamentarii , 2021 , 42 ( 12 ): 2664 - 2674 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.12.014 http://doi.org/10.3969/j.issn.1000-1093.2021.12.014 The target detection algorithms based on deep learning has become the mainstream of target detection in synthetic aperture radar images. Deep network algorithm often has a large number of parameters and don't run fast enough to meet real-time requirements，making it difficult to deploy on resource-constrained devices such as mobile terminal. Considering the requirements of real-time performance and portability of the model，Faster-RCNN for the two-stage target detection algorithm was improved to compare the influence of different improved methods on the speed and accuracy of algorithm.The lightweight model was optimized in combination with the characteristics of synthetic aperture radar (SAR) images，and finally compared with the single shot multibox detector for one-stage target detection algorithm.The experimental results show that the speed of the improved lightweight model is greatly improved while maintaining the original accuracy level，which can effectively meet the real-time requirements of SAR image target detection.

XU G , WANG X T , XU X G . Single image enhancement in sandstorm weather via tensor least square [J ] . IEEE/CAA Journal of Automatica Sinica , 2020 , 7 ( 6 ): 1649 - 1661 . DOI: 10.1109/JAS.2020.1003423 http://doi.org/10.1109/JAS.2020.1003423 https://ieeexplore.ieee.org/document/9239111/ https://ieeexplore.ieee.org/document/9239111/

CHENG Y Q , JIA Z H , LAI H C , et al . A fast sand-dust image enhancement algorithm by blue channel compensation and guided image filtering [J ] . IEEE Access , 2020 , 8 ( 5 ): 196690 - 196699 . DOI : 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

PARK T H , EOM I K . Sand-dust image enhancement using successive color balance with coincident chromatic histogram [J ] . IEEE Access , 2021 , 9 ( 7 ): 19749 - 19760 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

AL-AMEEN Z . Visibility enhancement for images captured in dusty weather via tuned tri-threshold fuzzy intensification operators [J ] . International Journal of Intelligent Systems Technologies & Applications , 2016 , 8 ( 8 ): 10 - 17 .

YANG Y , ZHANG C , LIU L L , et al . Visibility restoration of single image captured in dust and haze weather conditions [J ] . Multidimensional Systems and Signal Processing , 2020 , 31 ( 2 ): 619 - 633 . DOI: 10.1007/s11045-019-00678-z http://doi.org/10.1007/s11045-019-00678-z

HE K M , SUN J , TANG X O . Single image haze removal using dark channel prior [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2011 , 33 ( 12 ): 2341 - 2353 . DOI: 10.1109/TPAMI.2010.168 http://doi.org/10.1109/TPAMI.2010.168 In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

SHI Z H , FENG Y N , ZHAO M H , et al . Let you see in sand dust weather:a method based on halo-reduced dark channel prior dehazing for sand-dust image enhancement [J ] . IEEE Access , 2019 , 7 : 116722 - 116733 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

KIM S E , PARKT H , EOM I K . Fast single image dehazing using saturation based transmission map estimation [J ] . IEEE Transactions on Image Processing , 2019 , 29 : 1985 - 1998 . DOI: 10.1109/TIP.83 http://doi.org/10.1109/TIP.83 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83

于博文 , 吕明 . 改进的YOLOv3算法及其在军事目标检测中的应用 [J ] . 兵工学报 , 2022 , 43 ( 2 ): 345 - 354 . DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 复杂环境下军事目标检测技术是提高战场态势生成、分析能力的基础和关键。针对军事目标检测任务在复杂环境下传统检测算法的检测性能较低问题，提出一种基于改进YOLOv3的军事目标检测算法，通过深度学习实现复杂环境下军事目标的自动检测。构建军事目标图像数据集，为各类目标检测算法提供测试环境；在网络结构上通过引入可形变卷积改进的ResNet50-D残差网络作为特征提取网络，提高网络对形变目标的检测精度和速度；在特征融合阶段引入双注意力机制和特征重构模块，增强目标特征的表征能力，抑制干扰，提升检测精度；利用DIOU损失函数和Focal损失函数重新设计目标检测器的损失函数，进一步提高其对军事目标的检测精度；在军事目标图像数据集中进行测试实验。实验结果表明，改进的YOLOv3算法相比于原YOLOv3算法，平均精度均值提高了2.98%，检测速度提高了8.6帧/s，具有较好的检测性能，可为战场态势生成、分析提供有效的辅助技术支持。

YU B W , LÜ M . Improved YOLOv3 algorithm and its application in military target detection [J ] . Acta Armamentarii , 2022 , 43 ( 2 ): 345 - 354 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 Military target detection in a complex environment is the basis and key to improving battlefield situation generation and analysis capability. For the military target detection tasks, the detection performance of traditional detection algorithms in complex environment is low. A military target detection algorithm based on improved YOLOv3 algorithm is proposed to automatically detect the military targets in complex environment through deep learning. A military target image dataset is constructed to provide a testing environment for various target detection algorithms. The detection accuracy and speed of deformable target are improved by introducing the deformable convolutional improved ResNet50-D residual network as feature extraction network. In the stage of feature fusion, a dual-attention mechanism and feature reconstruction module are introduced to enhance the characterization ability of target features, suppress the interference, and improve the detection accuracy. The loss function of target detector is redesigned by using DIOU Loss functions and Focal Loss to funther improve the detection accuracy of military targets. The experimental results show that the improved YOLOv3 algorithm improves the average detection accuracy by 2.98% and the detection speed by 8.6 frames/s compared with the original YOLOv3 algorithm. The improved YOLOv3 algorithm has better detection performance and can provide effective auxiliary technical support for battlefield situation generation and analysis.

薛灵芝 , 曾向阳 , 杨爽 . 基于生成对抗网络的水声目标识别算法 [J ] . 兵工学报 , 2021 , 42 ( 11 ): 2444 - 2452 . DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 目标识别是水声探测领域的难题，也是研究热点。在水声目标识别实际应用中，标记样本数量不足是制约识别结果的主要因素之一。针对水声目标噪声数据具有的小样本特点，基于深度学习理论提出一种基于生成对抗网络的识别模型。该模型从生成模型与对抗模型的相互博弈中，学习更多有效的识别特征信息，并与深度自编码网络和深度置信网络模型进行对比。仿真实验结果表明：在样本数量有限的情况下，生成对抗网络模型的识别效果优于深度置信网络与深度自编码网络；3种深度学习模型的识别性能均优于先提取梅尔倒谱系数特征，再用Softmax分类的方法。为进一步测试所建模型的性能，研究了3种深度学习模型在不同信噪比下的鲁棒性，仿真实验结果表明：生成对抗网络模型对噪声具有更强的鲁棒性。

XUE L Z , ZENG X Y , YANG S . Underwater acoustic target recognition algorithm based on generative adversarial networks [J ] . Acta Armamentarii , 2021 , 42 ( 11 ): 2444 - 2452 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 In the practical application of underwater acoustic target recognition，one of the main factors restricting the recognition results is the insufficient quantity of labeled samples. For the small sample properties of underwater acoustic target noise，a generative adversarial networks(GAN)-based recognition algorithm is proposed based on deep learning theory. It can be used to learn more effective features with more discriminative information from the game between generated model and adversarial model，and it is compared with deep auto-encoder(DAE) network and deep belief network(DBN) models. The experimental results illustrate that the recognition performance of GAN network model is higher than those of DBN network and DAE network models when the number of samples is limited，and the recognition performances of the three deep learning models are better than the conventional approach of extracting Mel frequency cepstrum coefficient(MFCC) features and then classifying by Softmax. In addition，GAN network model is superior to DBN network and DAE network models in recognition rate when using training samples and test samples with different SNRs. The smulation experimental results indicate that the GAN network model is more robust to noise.

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ANCUTI C O , ANCUTI C . Single image dehazing by multi-scale fusion [J ] . IEEE Transactions on Image Processing , 2013 , 22 ( 8 ): 3271 - 3282 . Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. Our method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. We are the first to demonstrate the utility and effectiveness of a fusion-based technique for dehazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

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