曹强 , 曾晓利 , 程宗辉 , 等 . 基于机器视觉的飞机防护栅无损检测系统 [J ] . 无损检测 , 2016 , 38 ( 2 ): 24 - 27 ,51.

CAO Q , ZENG X L , CHENG Z H , et al. Nondestructive testing system for airplane protective griling based on machine vision [J ] . Nondestructive Testing , 2016 , 38 ( 2 ): 24 - 27 ,51. (in Chinese)

李国华 . 某型飞机防护栅套管固定螺栓断裂原因分析 [J ] . 科技创新导报 , 2015 , 12 ( 13 ): 44 - 45 .

LI G H . Analysis of the causes of the fracture of the fixing bolt of a certain aircraft protective grid casing [J ] . Science and Technology Innovation Herald , 2015 , 12 ( 13 ): 44 - 45 . (in Chinese)

刘平政 , 宋凯 , 宁宁 , 等 . 飞机紧固件孔周裂纹检测远场涡流传感器设计及优化 [J ] . 仪器仪表学报 , 2019 , 40 ( 6 ): 1 - 8 .

LIU P Z , SONG K , NING N , et al. Design and optimization of a far-field eddy current sensor for crack detection around the hole of aircraft fasteners [J ] . Chinese Journal of Scientific Instrument , 2019 , 40 ( 6 ): 1 - 8 . (in Chinese)

TAVARES S M O , CASTRO P M S T . An overview of fatigue in aircraft structures [J ] . Fatigue & Fracture of Engineering Materials & Structures , 2017 , 40 ( 10 ): 1510 - 1529 .

寇光杰 , 杨正伟 , 贾庸 , 等 . 复杂型面叶片裂纹的超声红外热成像检测 [J ] . 红外与激光工程 , 2019 , 48 ( 12 ): 101 - 109 .

KOU G J , YANG Z W , JIA Y , et al. Ultrasonic infrared thermal imaging detection of cracks in complex-shaped blades [J ] . Infrared and Laser Engineering , 2019 48 ( 12 ): 101 - 109 . (in Chinese)

黄凤英 . 钢轨表面裂纹涡流检测定量评估方法 [J ] . 中国铁道科学 , 2017 , 38 ( 2 ): 28 - 33 .

HUANG F Y . Quantitative evaluation method for eddy current detection of rail surface cracks [J ] . China Railway Science , 2017 , 38 ( 2 ): 28 - 33 . (in Chinese)

UCHANIN V , OSTASH O . Development of electromagnetic NDT methods for structural integrity assessment [J ] . Procedia Structural Integrity , 2019 , 16 : 192 - 197 . DOI: 10.1016/j.prostr.2019.07.040 http://doi.org/10.1016/j.prostr.2019.07.040 https://linkinghub.elsevier.com/retrieve/pii/S2452321619301489 https://linkinghub.elsevier.com/retrieve/pii/S2452321619301489

CHA Y J , CHOI W , BÜYÜKÖZTÜRK O . Deep learning-based crack damage detection using convolutional neural networks [J ] . Computer-Aided Civil and Infrastructure Engineering , 2017 , 32 ( 5 ): 361 - 378 . DOI: 10.1111/mice.2017.32.issue-5 http://doi.org/10.1111/mice.2017.32.issue-5 http://doi.wiley.com/10.1111/mice.2017.32.issue-5 http://doi.wiley.com/10.1111/mice.2017.32.issue-5

PRIYADUMKOL J , KITTICHAIKARN C , THAINIMIT C . Crack detection on unwashed eggs using image processing [J ] . Journal of Food Engineering , 2017 , 209 : 76 - 82 . DOI: 10.1016/j.jfoodeng.2017.04.015 http://doi.org/10.1016/j.jfoodeng.2017.04.015 https://linkinghub.elsevier.com/retrieve/pii/S0260877417301620 https://linkinghub.elsevier.com/retrieve/pii/S0260877417301620

陈波 , 张华 , 汪双 , 等 . 基于全卷积神经网络的坝面裂纹检测方法研究 [J ] . 水力发电学报 , 2020 , 39 ( 7 ): 52 - 60 .

CHEN B , ZHANG H , WANG S , et al. Research on dam surface crack detection method based on full convolutional neural network [J ] . Journal of Hydroelectric Engineering , 2020 , 39 ( 7 ): 52 - 60 . (in Chinese)

ZHENG Z , QI H Y , ZHUANG L , et al. Automated rail surface crack analytics using deep data-driven models and transfer learning [J ] . Sustainable Cities and Society , 2021 , 70 : 102898 . DOI: 10.1016/j.scs.2021.102898 http://doi.org/10.1016/j.scs.2021.102898 https://linkinghub.elsevier.com/retrieve/pii/S2210670721001864 https://linkinghub.elsevier.com/retrieve/pii/S2210670721001864

王森 , 伍星 , 张印辉 , 等 . 基于深度学习的全卷积网络图像裂纹检测 [J ] . 计算机辅助设计与图形学学报 , 2018 , 30 ( 5 ): 859 - 867 .

WANG S , WU X , ZHANG Y H , et al. Fully convolutional network image crack detection based on deep learning [J ] . Journal of Computer Aided Design and Graphics , 2018 , 30 ( 5 ): 859 - 867 . (in Chinese)

吕帅帅 , 杨宇 , 王彬文 , 等 . 基于改进Mask-RCNN的飞行器结构裂纹自动检测方法 [J ] . 振动.测试与诊断 , 2021 , 41 ( 3 ): 487 - 494 .

LÜ S S , YANG Y , WANG B W , et al. Automatic detection method of aircraft structure cracks based on improved Mask-RCNN [J ] . Vibration. Test and Diagnosis , 2021 , 41 ( 3 ): 487 - 494 . (in Chinese)

申铉京 , 张赫 , 陈海鹏 , 等 . 快速递归多阈值分割算法 [J ] . 吉林大学学报(工学版) , 2016 , 46 ( 2 ): 528 - 534 .

SHEN X J , ZHANG H , CHEN H P , et al. Fast recursive multi-threshold segmentation algorithm [J ] . Journal of Jilin University (Engineering and Technology Edition) , 2016 , 46 ( 2 ): 528 - 534 . (in Chinese)

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

孙皓泽 , 常天庆 , 王全东 , 等 . 一种基于分层多尺度卷积特征提取的坦克装甲目标图像检测方法 [J ] . 兵工学报 , 2017 , 38 ( 9 ): 1681 - 1691 . DOI: 10.3969/j.issn.1000-1093.2017.09.003 http://doi.org/10.3969/j.issn.1000-1093.2017.09.003 针对坦克装甲目标的图像检测任务，提出一种基于分层多尺度卷积特征提取的目标检测方法。采用迁移学习的设计思路，在VGG-16网络的基础上针对目标检测任务对网络的结构和参数进行修改和微调，结合建议区域提取网络和目标检测子网络来实现对目标的精确检测。对于建议区域提取网络，在多个不同分辨率的卷积特征图上分层提取多种尺度的建议区域，增强对弱小目标的检测能力；对于目标检测子网络，选用分辨率更高的卷积特征图来提取目标，并额外增加了一个上采样层来提升特征图的分辨率。通过结合多尺度训练、困难负样本挖掘等多种设计和训练方法，所提出的方法在构建的坦克装甲目标数据集上取得了优异的检测效果，目标检测的精度和速度均优于目前主流的检测方法。

SUN H Z , CHANG T Q , WANG Q D , et al. Image detection method for tank and armored targets based on hierarchical multi-scale convolution feature extraction [J ] . Acta Armamentarii , 2017 , 38 ( 9 ): 1681 - 1691 . (in Chinese)

王伟平 , 王琦 , 于洋 . 基于注意力机制与深度学习算法的机床主轴系统故障辨识 [J ] . 兵工学报 , 2022 , 43 ( 4 ): 861 - 875 . DOI: 10.12382/bgxb.2021.0202 http://doi.org/10.12382/bgxb.2021.0202 针对具有复杂非线性特点的数控机床主轴系统整体动态退化故障较难辨识及故障研究难度大的问题，从数据分析入手，提出一种基于注意力机制与深度学习算法的智能化故障辨识方法，研究机床主轴系统的整体故障辨识问题。该方法设计了注意力机制的研究框架，将研究问题分为全局纵向大分类区间和局部横向细粒度区间两个维度：采用训练并调优后推理平均绝对误差达到0.028 7的门控循环单元模型，辨识出大分类区间的全局性退化故障；采用鲁棒性强且辨识准确率达99.7%的残差网络模型，在sym8小波基自适应软阈值降噪的基础上对局部细粒度区间故障进行准确细节辨识。结果表明：该方法可量化地辨识出主轴系统的整体故障；所提注意力机制可使大分类区间无法准确辨识的故障在细粒度区间得到有效区分，类内数据增长梯度由6.6%增加到43.8%；通过对机床主轴系统实际使用中在空载状态下遇到的不对中和局部共振等典型故障，以及在负载加工状态下故障的辨识研究，验证了所提方法的有效性与准确性。

WANG W P , WANG Q , YU Y . Fault identification of machine tool spindle system based on attention mechanism and deep learning algorithm [J ] . Acta Armamentarii , 2022 , 43 ( 4 ): 861 - 875 . (in Chinese) DOI: 10.12382/bgxb.2021.0202 http://doi.org/10.12382/bgxb.2021.0202 The overall dynamic degradation fault of the numerically-controlled machine tool spindle system with complex nonlinear characteristics is difficultly identified and investigated.An intelligent fault identification method based on attention mechanism and depth learning algorithm is proposed to stud the overall fault identification of spindle system by starting with data analysis.The proposed method is used to design the research framework of attention mechanism，and divide the research problems into global vertical large classification interval dimension and local horizontal fine-grained interval dimension.The gated recurrent unit model with reasoning average absolute error of 0.028 7 after training and tuning is used to identify the global degradation faults in large classification interval.The residual network model with strong robustness and identification accuracy of 99.7% is used to accurately identify the local fine-grained interval faults， based on sym8 wavelet basis adaptive soft threshold noise reduction.The results show that the proposed method is used to quantitatively identify the overall fault of spindle system. The proposed attention mechanism is used to effectively distinguish the faults that cannot be accurately identified in the large classification interval in the fine-grained interval，and the data growth gradient in the category increases from 6.6% to 43.8%. The effectiveness and accuracy of the proposed method are verified by studying the typical faults，such as misalignment and local resonance encountered in the actual use of the machine tool spindle system under no-load，and the fault identification under loading.

曹磊 , 王强 , 史润佳 , 等 . 基于改进RPN的Faster-RCNN网络SAR图像车辆目标检测方法 [J ] . 东南大学学报(自然科学版) , 2021 , 51 ( 1 ): 87 - 91 .

CAO L , WANG Q , SHI R J , et al. Faster-RCNN network SAR image vehicle target detection method based on improved RPN [J ] . Journal of Southeast University (Natural Science Edition) , 2021 , 51 ( 1 ): 87 - 91 . (in Chinese)