吕宁 , 徐更光 . 基于工业计算机断层成像的装药底隙无损检测方法研究 [J ] . 兵工学报 , 2015 , 36 ( 1 ): 157 - 162 . DOI: 10.3969/j.issn.1000-1093.2015.01.023 http://doi.org/10.3969/j.issn.1000-1093.2015.01.023 在弹药装药底隙检测中，运用密度对比法结合体积效应，将底隙测量转化为弹底密度变化的测量，建立弹底扫描断层中心部位计算机断层成像（CT）值变化与底隙值之间的数学模型，解决底隙测量理论与实际操作中的难题。预置底隙测量实验结果表明：该模型可完成对0.20~0.50 mm底隙的测量，最大相对误差小于3.5%，可实现对装药底隙的定性判断与定量检测。

LYU N , XU G G . Inspection of base separation of charge by industrial CT imaging [J ] . Acta Armamentarii , 2015 , 36 ( 1 ): 157 - 162 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2015.01.023 http://doi.org/10.3969/j.issn.1000-1093.2015.01.023 A volume effect of industrial computed tomography(CT) imaging and a contrast method for density inspection are used to build an algebraic model for base separation inspection, which improves the base separation inspection method in theoretical and practical ways. The size information is qualitatively and quantitatively analyzed with high precision by the fluctuation of inspected CT values. The experimental results of base separation inspection show that the density contrast model is suitable for the inspection of 0.20~0.50 mm base separation, and the relative error is less than 3.5%.

廖达海 , 殷明帅 , 罗宏斌 , 等 . 基于耦合去噪算法的航空发动机中Si3N4圆柱滚子表面缺陷的检测方法 [J ] . 兵工学报 , 2022 , 43 ( 1 ): 190 - 198 .

LIAO D H , YIN M S , LUO H B , et al. Detection and analysis of surface defects of Si3N4 cylindrical roller in aero-engine based on coupled denoising algorithm [J ] . Acta Armamentarii , 2022 , 43 ( 1 ): 190 - 198 . (in Chinese)

卢洪义 , 陈庆贵 , 周红梅 , 等 . 固体火箭发动机CT图像条状伪影校正 [J ] . 航空动力学报 , 2016 , 31 ( 10 ): 2515 - 2521 .

LU H Y , CHEN Q G , ZHOU H M , et al. Streak artifacts correction of solid rocket motor’s CT image [J ] . Journal of Aerospace Power , 2016 , 31 ( 10 ): 2515 - 2521 . (in Chinese)

黄鹏 , 郑淇 , 梁超 . 图像分割方法综述 [J ] . 武汉大学学报(理学版) , 2020 , 66 ( 6 ): 519 - 531 .

HUANG P , ZHENG Q , LIANG C . Overview of image segmentation methods [J ] . Journal of Wuhan University (Natural Science Edition) , 2020 , 66 ( 6 ): 519 - 531 . (in Chinese)

吴一全 , 吴加明 , 占必超 . 一种可有效分割小目标图像的阈值选取方法 [J ] . 兵工学报 , 2011 , 32 ( 4 ): 469 - 475 . 目标检测与识别中常遇到目标与背景大小之比很小的小目标图像分割问题，此时现有的阈值分割方法几乎都失效。为此，提出了一种基于背景与目标的面积差和类内方差的小目标图像分割阈值选取方法。指出了目前图像阈值分割方法不能有效分割小目标图像这一缺陷，给出了基于背景与目标面积差和类内方差的一维直方图、二维直方图区域直分及更为有效的二维直方图区域斜分阈值选取公式，导出了相应二维斜分阈值选取的快速递推算法；最后在实验结果中给出了本文方法的图像阈值分割结果和运行时间，并与Otsu、最大熵及Fisher阈值选取快速方法进行了比较。结果表明：本文方法能准确地分割小目标图像，且运行时间短，抗噪性好。

WU Y Q , WU J M , ZHAN B C . An effective method of threshold selection for small object image [J ] . Acta Armamentarii , 2011 , 32 ( 4 ): 469 - 475 . (in Chinese)

朱敏 , 卢洪义 , 肖志斌 , 等 . 固体发动机CT图像的一种自动分割方法 [J ] . 固体火箭技术 , 2008 ( 2 ): 201 - 204 .

ZHU M , LU H Y , XIAO Z B , et al. A kind of automatic segmentation method for solid motor CT image [J ] . Journal of Solid Rocket Technology , 2008 ( 2 ): 201 - 204 . (in Chinese)

于光辉 , 卢洪义 , 朱敏 , 等 . 基于引力模型的固体发动机CT图像边缘检测 [J ] . 核电子学与探测技术 , 2012 , 32 ( 2 ): 184 - 187 .

YU G H , LU H Y , ZHU M , et al. Edge detection of solid motor CT images based on gravity model [J ] . Nuclear Electronics & Detection Technology , 2012 , 32 ( 2 ): 184 - 187 . (in Chinese)

郑洲 , 张学昌 , 郑四鸣 , 等 . 基于区域增长与统一化水平集的CT肝脏图像分割 [J ] . 浙江大学学报(工学版) , 2018 , 52 ( 12 ): 2382 - 2396 .

ZGENG Z , ZHANG X C , ZHENG S M , et al. Liver segmentation in CT images based on region-growing and unified level set method [J ] . Journal of Zhejiang University (Engineering Science) , 2018 , 52 ( 12 ): 2382 - 2396 . (in Chinese)

张丽娟 , 章润 , 李东明 , 等 . 积神经网络交互分割肝脏CT图像 [J ] . 液晶与显示 , 2021 , 36 ( 9 ): 1294 - 1304 .

ZHANG L J , ZHANG R , LI D M , et al. Region-growing fully convolutional neural network interactive segmentation of liver CT images [J ] . Chinese Journal of Liquid Crystals and Displays , 2021 , 36 ( 9 ): 1294 - 1304 . (in Chinese) DOI: 10.37188/CJLCD.2020-0338 http://doi.org/10.37188/CJLCD.2020-0338 http://cjlcd.lightpublishing.cn/thesisDetails#10.37188/CJLCD.2020-0338 http://cjlcd.lightpublishing.cn/thesisDetails#10.37188/CJLCD.2020-0338

谢勤岚 , 潘先攀 . 自适应形状约束Graph cuts算法在腹部CT图像分割中的应用 [J ] . 中南民族大学学报(自然科学版) , 2019 , 38 ( 1 ): 119 - 125 .

XIE Q L , PAN X P . Adaptive shape constraint based graph cuts and its application on CT images segmentation [J ] . South-Central Minzu University(Natural Science Edition , 2019 , 38 ( 1 ): 119 - 125 . (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.

张玉燕 , 李永保 , 温银堂 , 等 . 基于Faster R-卷积神经网络的金属点阵结构缺陷识别方法 [J ] . 兵工学报 , 2019 , 40 ( 11 ): 2329 - 2335 . DOI: 10.3969/j.issn.1000-1093.2019.11.018 http://doi.org/10.3969/j.issn.1000-1093.2019.11.018 采用增材制造技术制备的金属三维点阵结构可能存在裂纹、未熔合、断层等缺陷，导致金属点阵结构的结构-功能性能下降，为此提出一种金属三维多层点阵结构内部缺陷的检测方法。在Faster R-卷积神经网络架构基础上设计特征提取网络，结合工业CT扫描图片，对得到的断层灰度图像中缺陷部位进行快速、准确、智能检测识别和定位。实验验证结果表明，对金属三维多层点阵结构样件的内部典型缺陷识别率达到99.5%.

ZHANG Y Y , LI Y B , WEN Y T , et al. Internal defect detection of metal three-dimensional multi-layer lattice structure based on faster R-CNN [J ] . Acta Armamentarii , 2019 , 40 ( 11 ): 2329 - 2335 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2019.11.018 http://doi.org/10.3969/j.issn.1000-1093.2019.11.018 The cracks, incomplete fusion, faults and other defects may exist in the metal three-dimensional lattice structure prepared by additive manufacturing technology, which lead to the decline of structure-functional performance of metal lattice structure. A Faster R-CNN-based internal defect detection method is proposed for metal three-dimensional multi-layer lattice structure. A feature extraction network is designed on the basis of the Faster R-CNN network architecture. It makes the defects in the obtained gray-scale image and the CT scanning image be detected and positioned quickly, accurately and intelligently. The experimental results show that the recognition rate of the typical internal defects of metal three-dimensional multi-layer lattice structure sample is 99.5%. Key

TREMEAU A , BOREL N . A region growing and merging algorithm to color segmentation [J ] . Pattern Recognition , 1997 , 30 ( 7 ): 1191 - 1203 . DOI: 10.1016/S0031-3203(96)00147-1 http://doi.org/10.1016/S0031-3203(96)00147-1 https://linkinghub.elsevier.com/retrieve/pii/S0031320396001471 https://linkinghub.elsevier.com/retrieve/pii/S0031320396001471

彭道刚 , 尹磊 , 戚尔江 , 等 . 基于OTSU和区域生长的电厂管道缺陷检测与分割 [J ] . 红外技术 , 2021 , 43 ( 5 ): 502 - 509 .

PENG D G , YIN L , QI E J , et al. Power plant pipeline defect detection and segmentation based on OTSU’s and region growing algorithms [J ] . Infrared Technology , 2021 , 43 ( 5 ): 502 - 509 . (in Chinese)

余成波 , 曾亮 , 张林 . 基于OTSU和区域生长的电气设备多点故障分割 [J ] . 红外技术 , 2018 , 40 ( 10 ): 1008 - 1012 .

YU C B , ZENG L , ZHANG L . Multi point fault segmentation of electrical equipment based on OTSU and region growth [J ] . Infrared Technology , 2018 , 40 ( 10 ): 1008 - 1012 . (in Chinese)

万俊霞 , 林珊玲 , 梅婷 , 等 . 基于图像分割和动态直方图均衡的电润湿显示器图像增强算法 [J ] . 光子学报 , 2022 , 51 ( 2 ): 240 - 250 .

WAN J X , LIN S L , MEI T , et al. Image enhancement algorithm of electrowetting displays based on image segmentation and dynamic histogram equalization [J ] . Acta Photonica Sinica , 2022 , 51 ( 2 ): 240 - 250 . (in Chinese)

YANG J , FAN J F , AI D N , et al. Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image [J ] . Neurocomputing , 2016 , 195 : 88 - 95 . DOI: 10.1016/j.neucom.2015.05.140 http://doi.org/10.1016/j.neucom.2015.05.140 https://linkinghub.elsevier.com/retrieve/pii/S0925231216001223 https://linkinghub.elsevier.com/retrieve/pii/S0925231216001223

刘健庄 , 栗文青 . 灰度图像的二维 Otsu 自动阈值分割法 [J ] . 自动化学报 , 1993 , 19 ( 1 ): 101 - 105 .

LIU J Z , LI W Q . The automatic thresholding of gray level pictures via two-dimensional Otsu method [J ] . Ata Automatica Sinica , 1993 , 19 ( 1 ): 101 - 105 . (in Chinese)

LI C H , LEE C K . Minimum cross entropy thresholding [J ] . Pattern Recognition , 1993 , 26 ( 4 ): 617 - 625 . DOI: 10.1016/0031-3203(93)90115-D http://doi.org/10.1016/0031-3203(93)90115-D https://linkinghub.elsevier.com/retrieve/pii/003132039390115D https://linkinghub.elsevier.com/retrieve/pii/003132039390115D

雷博 , 范九伦 . 灰度图像的二维交叉熵阈值分割法 [J ] . 光子学报 , 2009 , 38 ( 6 ): 1572 - 1576 .

LEI B , FAN J L . Two-dimensional cross-entropy thresholding segmentation method [J ] . Acta Photonica Sinica , 2009 , 38 ( 6 ): 1572 - 1576 . (in Chinese)