[1] 裴广达. 孔探与现代航空发动机维护[J]. 航空工程与维修, 2000(5):27-28.(PEI G D. Borescope inspection and modern aeroengine maintenance[J]. Aviation Maintenance and Engineering,2000(5):27-28.) [2] 丁莉芬. 机器视觉在航空发动机内窥检测中的应用研究[D]. 天津:中国民用航空学院,2005.(DING L F. Research on application of computer vision in hole-detection of aero-engine[D]. Tianjin:Civil Aviation University of China,2005.) [3] SHEN Z J,WAN X L,YE F,et al. Deep learning based framework for automatic damage detection in aircraft engine borescope inspection[C]//Proceedings of the 2019 International Conference on Computing, Networking and Communications. Piscataway:IEEE,2019:1005-1010. [4] 李华, 陈果, 陈新波,等. 航空发动机内部裂纹自动测量方法研究[J]. 计算机工程与应用,2016,52(11):233-237.(LI H, CHEN G,CHEN X B,et al. Study on automatic measurement method for aero-engine inner damage crack[J]. Computer Engineering and Applications,2016,52(11):233-237.) [5] 李华, 陈果, 林桐, 等. 航空发动机叶片损伤自动测量方法研究[J]. 航空计算技术,2015,45(1):52-55.(LI H,CHEN G,LIN T,et al. Study on automatic measurement method for damage aero engine lamina[J]. Aeronautical Computing Technique,2015,45(1):52-55.) [6] SIMONYAN K,ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-09-12]. https://arxiv.org/pdf/1409.1556.pdf. [7] LONG J,SHELHAMER E,DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2015:3431-3440. [8] REN S Q,HE K M,GIRSHICK R,et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017, 39(6):1137-1149. [9] LIU W,ANGUELOV D,ERHAN D,et al. SSD:single shot MultiBox detector[C]//Proceedings of the 2016 European Conference on Computer Vision,LNCS 9905. Cham:Springer, 2016:21-37. [10] HE K M,GKIOXARI G,DOLLÁR P,et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE,2017:2980-2988. [11] 李长有, 马齐爽, 姚红宇. 基于小波变换的孔探图像边缘粗糙度分析[J]. 北京航空航天大学学报,2007,33(6):705-708. (LI C Y,MA Q S,YAO H Y. Analysis of coarseness of edges extracted from borescope images based on wavelet transform[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007,33(6):705-708.) [12] 张勇, 刘冠军, 邱静. 基于图像自动测量的航空发动机故障检测技术研究[J]. 机械科学与技术,2008,27(2):176-179. (ZHANG Y,LIU G J,QIU J. Aeroengine's fault detection technology based on image automatic measurement[J]. Mechanical Science and Technology for Aerospace Engineering, 2008,27(2):176-179.) [13] 陈果, 汤洋. 基于孔探图像纹理特征的航空发动机损伤识别方法[J]. 仪器仪表学报,2008,29(8):1709-1713.(CHEN G, TANG Y. Aero-engine interior damage recognition based on texture features of borescope image[J]. Chinese Journal of Scientific Instrument,2008,29(8):1709-1713.) [14] 孟娇茹, 王娟. 孔探成像在发动机损伤检测中的应用研究[J]. 节能技术,2009,27(1):69-73. (MENG J R,WANG J. Research on application of video borescope during engine inspection[J]. Energy Conservation Technology,2009,27(1):69-73.) [15] SVENSÉN M,HARDWICK D S,POWRIE H E G. Deep neural networks analysis of borescope images[J]. PHM Society European Conference,2018,4(1):No. 401. [16] KIM Y H,LEE J R. Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing[J]. Structural Health Monitoring,2019,18(5/6):2020-2039. [17] BIAN X,LIM S N,ZHOU N. Multiscale fully convolutional network with application to industrial inspection[C]//Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision. Piscataway:IEEE,2016:1-8. [18] 旷可嘉. 深度学习及其在航空发动机缺陷检测中的应用研究[D]. 广州:华南理工大学,2017.(KUANG K J. Research on deep learning and its application on the defects detection for aero engine[D]. Guangzhou:South China University of Technology, 2017.) [19] 樊玮, 段博坤, 黄睿, 等. 基于风格迁移的交互式航空发动机孔探图像扩展方法[J]. 计算机应用,2020,40(12):3631-3636. (FAN W, DUAN B K, HUANG R, et al. Interactive augmentation method for aircraft engine borescope inspection images based on style transfer[J]. Journal of Computer Applications,2020,40(12):3631-3636.) [20] DENG J,DONG W,SOCHER R,et al. ImageNet:a large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2009:248-255. [21] BOCHKOVSKIY A,WANG C Y,LIAO H Y M. YOLOv4:optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2020-09-12]. https://arxiv.org/pdf/2004.10934.pdf. [22] LIN T Y,MAIRE M,BELONGIE S,et al. Microsoft COCO:common objects in context[C]//Proceedings of the 2014 European Conference on Computer Vision,LNCS 8693. Cham:Springer, 2014:740-755. [23] REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once:unified,real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:779-788. |