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纸质出版日期： 2023-10-16 ，
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黄睿， 成旭毅， 汪若飞， 段博坤， 陈晓璐， 樊玮. 2023. 自适应参数分析结果的航空发动机孔探图像损伤检测. 中国图象图形学报， 28(10):3136-3148
Huang Rui， Cheng Xuyi， Wang Ruofei， Duan Bokun， Chen Xiaolu， Fan Wei. 2023. Adaptive parameters based borescope image-related damage detection of aeroengine. Journal of Image and Graphics， 28(10):3136-3148
目的
2
航空发动机孔探图像的损伤检测关系到航空发动机是否要非例行更换，直接影响飞机的飞行安全和利用率。现有的孔探图像损伤检测方法直接使用目标检测方法训练一个多类别损伤检测器，使用相同的参数在不同位置检测损伤。由于没有考虑同类型损伤在发动机不同区域发生概率的不同，导致现有方法的检测准确率较低。为了提高损伤检测的准确率，提出了一种自适应参数的航空发动机孔探图像损伤检测方法。
方法
2
通过识别孔探图像所属的发动机区域，针对不同区域孔探图像设置不同的参数用于检测发动机损伤。同时为了避免单检测器上不同类型损伤之间相互干扰，采用独立检测器检测单一类型的损伤，并对误检率高的损伤进行真假识别。通过合并检测到的不同类型的损伤，得到最终的损伤检测结果。此外，为了改进水平的矩形检测框，使用分割结果产生旋转的检测框，有效地减少了框中的背景区域。
结果
2
在13个航空发动机区域的2 654幅孔探图像上针对烧蚀、裂缝、材料丢失、涂层脱落、刻痕和凹坑等6种典型的发动机损伤进行检测实验。提出的损伤检测方法在准确率和召回率两方面分别达到了90.4%和90.7%，相较于目标检测方法YOLOv5（you only look once version 5）的准确率和召回率高24.8%和25.1%。实验结果表明，本文方法在航空发动机损伤检测方面优于其他对比方法。
结论
2
本文所提出的自适应参数的航空发动机损伤检测模型通过识别发动机图像所属的部位，针对同种类型的损伤检测器设定不同的参数，有效地提高了检测器的检测性能。同时，针对容易误检的裂缝、刻痕和凹坑增加了真假损伤判别器，有效地减少了误检的情况。
Objective
2
Borescope image-based damage detection of aeroengine is focused on its non-routine change， which can affect the flight safety and utilization rate of the aircraft straightforward. Current detection methods can be used to train a multifaceted off-the-shelf object detector-relevant damage using the same parameters for multiple areas. The detection accuracy of the existing detection methods is required to be optimized for resolving the problem of occurrence probabilities of the same type of damage in related to multiple areas of aeroengine. Furthermore， its detector may misclassify a seam or a scratch into a crack， or misidentify a nick into a dent， which affects the detection performance as well. To improve its accuracy of the damage detection， we develop an adaptive parameters-related aeroscope image damage detection method， which can detect such damages through borescope images-related multiple parameters for different areas. Furthermore， an independent detector is proposed and illustrated to detect single-class damage to avoid interference for a single detector from different types of damages， and a high miss-classification rate-relevant true and false identification is carried out for such damages detection. The final detection result can be obtained via the integrated detection results of different damage detectors.
Method
2
The proposed method is a based on the integration of a region recognizer， several object detectors， true and false identifiers， and a rotated bounding box generator. Specifically， to classify an aeroengine image into one of thirteen aeroengine parts， area-related recognizer is an image classifier in terms of Pytorch-Encoding. The YOLOv5 is selected as a benched single-damage detector for its high speed and precision. A single damage detector for each type of damage is trained for the expansion of a novel type of damage. The detectors are well-trained and tested on different areas of aeroengine in terms of the changeable super-parameters. The parameters are configured out according to the classification result of our recognizer in the inference process. A high miss-classification rate-related method is focused on true and false identifiers to damages analysis as well. The ResNet101 is chosen as the identifier to filter out fake cracks and identify nick and dent. We redraw the detected bounding boxes of missing-tbc into the rotated bounding boxes via segmentation of the damage areas. The rotated bounding box can be used to cover the key area of the missing-tbc tightly， and background image areas can be cut it out effectively.
Result
2
Comparative analysis is carried out with five sort of popular object detectors， called SSD， YOLACT， YOLOv5， YOLOX， and MaskRCNN. All the experiments are related to 2 654 borescope images from 13 aeroengine areas， which include six typical aeroengine damage types of burn， crack， missing material， missing-tbc， nick， and dent. The accuracy is used to evaluate the performance of our recognizer， and such of mean average precision （mAP）， F-measure， accuracy， recall， and false positive is adopted for evaluating different objectors. Our detection method is employed relevant to region classification， damage detection， and several ablation studies as well. The average accuracy of recognizer on all detected areas is 95.35%. And， the accuracies of combustion chamber （CC）， high pressure turbine （HPT）， and high pressure turbine nozzle （HPTN） are higher than 99%. The mAP of our method on all types of damages is 56.3%， which is higher than the mAPs of YOLOv5 and SSD by 15.2% and 39.7%. The proposed damage detection method achieves 90.4% and 90.7% in terms of accuracy and recall， and YOLOv5 is optimized by 24.8% and 25.1%. The results demonstrate that the same super-parameters setting are still to be more optimal for borescope damage detection. For example， each of optimal confidence thresholds on HPT， high pressure compressor （HPC）， and CC are 0.7， 0.2～0.6， and 0.6. We also compare the performance of our method of true and false identification excluded. The false-positive detection can be decreased using true and false identification.
Conclusion
2
To improve its detection performance， an adaptive parameters set based borescope image damage detection of aeroengine is proposed for a damage detector at area-related scale. Furthermore， a high miss-classification rate-relevant true and false identification can be adopted for the damages as well.
航空发动机损伤检测目标检测孔探图像自适应参数
aeroenginedamage detectionobject detectionborescope imageadaptive parameter
Bian X， Lim S N and Zhou N. 2016. Multiscale fully convolutional network with application to industrial inspection//Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision （WACV）. Lake Placid， USA： IEEE： 1-8 ［DOI： 10.1109/WACV.2016.7477595http://dx.doi.org/10.1109/WACV.2016.7477595］
Bolya D， Zhou C， Xiao F Y and Lee Y J. 2019. YOLACT： real-time instance segmentation//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 9156-9165 ［DOI： 10.1109/ICCV.2019.00925http://dx.doi.org/10.1109/ICCV.2019.00925］
Cai Z W and Vasconcelos N. 2018. Cascade R-CNN： delving into high quality object detection//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 6154-6162 ［DOI： 10.1109/CVPR.2018.00644http://dx.doi.org/10.1109/CVPR.2018.00644］
Chen G and Tang Y. 2008. Aero-engine interior damage recognition based on texture features of borescope image. Chinese Journal of Scientific Instrument， 29（8）： 1709-1713
陈果， 汤洋. 2008. 基于孔探图像纹理特征的航空发动机损伤识别方法. 仪器仪表学报， 29（8）： 1709-1713
Chen H， Sun K Y， Tian Z， Shen C H， Huang Y M and Yan Y L. 2020. BlendMask： top-down meets bottom-up for instance segmentation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 8570-8578 ［DOI： 10.1109/CVPR42600.2020.00860http://dx.doi.org/10.1109/CVPR42600.2020.00860］
Chen K， Pang J M， Wang J Q， Xiong Y， Li X X， Sun S Y， Feng W S， Liu Z W， Shi J P， Ouyang W L， Loy C C and Lin D H. 2019. Hybrid task cascade for instance segmentation//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Long Beach， USA： IEEE： 4969-4978 ［DOI： 10.1109/CVPR.2019.00511http://dx.doi.org/10.1109/CVPR.2019.00511］
Dai J F， He K M， Li Y， Ren S Q and Sun J. 2016a. Instance-sensitive fully convolutional networks//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 534-549 ［DOI： 10.1007/978-3-319-46466-4_32http://dx.doi.org/10.1007/978-3-319-46466-4_32］
Dai J F， He K M and Sun J. 2016b. Instance-aware semantic segmentation via multi-task network cascades//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Las Vegas， USA： IEEE： 3150-3158 ［DOI： 10.1109/CVPR.2016.343http://dx.doi.org/10.1109/CVPR.2016.343］
Dai J F， Li Y， He K M and Sun J. 2016c. R-FCN： object detection via region-based fully convolutional networks//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona， Spain： Curran Associates Inc.： 379-387
Fan W， Duan B K， Huang R， Liu T and Zhang N. 2020. Interactive augmentation method for aircraft engine borescope inspection images based on style transfer. Journal of Computer Applications， 40（12）： 3631-3636
樊玮， 段博坤， 黄睿， 刘挺， 张宁. 2020. 基于风格迁移的交互式航空发动机孔探图像扩展方法. 计算机应用， 40（12）： 3631-3636 ［DOI： 10.11772/j.issn.1001-9081.2020040585http://dx.doi.org/10.11772/j.issn.1001-9081.2020040585］
Ge Z， Liu S T， Wang F， Li Z M and Sun J. 2021. YOLOX： exceeding YOLO series in 2021 ［EB/OL］. ［2022-07-08］. http：//arxiv.org/pdf/2107.08430.pdfhttp://arxiv.org/pdf/2107.08430.pdf
Girshick R. 2015. Fast R-CNN//Proceedings of 2015 IEEE International Conference on Computer Vision （ICCV）. Santiago， Chile： IEEE： 1440-1448 ［DOI： 10.1109/ICCV.2015.169http://dx.doi.org/10.1109/ICCV.2015.169］
Hayder Z， He X M and Salzmann M. 2017. Boundary-aware instance segmentation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Honolulu， USA： IEEE： 587-595 ［DOI： 10.1109/CVPR.2017.70http://dx.doi.org/10.1109/CVPR.2017.70］
He K M， Gkioxari G， Dollr P and Girshick R. 2017. Mask R-CNN//Proceedings of 2017 IEEE International Conference on Computer Vision （ICCV）. Venice， Italy： IEEE： 2980-2988 ［DOI： 10.1109/ICCV.2017.322http://dx.doi.org/10.1109/ICCV.2017.322］
He K M， Zhang X Y， Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 770-778 ［DOI： 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90］
Huang Z J， Huang L C， Gong Y C， Huang C and Wang X G. 2019. Mask scoring R-CNN//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Long Beach， USA： IEEE： 6402-6411 ［DOI： 10.1109/CVPR.2019.00657http://dx.doi.org/10.1109/CVPR.2019.00657］
Kim Y H and Lee J R. 2019. Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing. Structural Health Monitoring， 18（5/6）： 2020-2039 ［DOI： 10.1177/1475921719830328http://dx.doi.org/10.1177/1475921719830328］
Kuang K J. 2017. Research on Deep Learning and Its Application on the Defects Detection for Aero Engine. Guangzhou： South China University of Technology
旷可嘉. 2017. 深度学习及其在航空发动机缺陷检测中的应用研究. 广州： 华南理工大学
Law H and Deng J. 2018. CornerNet： detecting objects as paired keypoints//Proceedings of the 15th European Conference on Computer Vision. Munich， Germany： Springer： 765-781 ［DOI： 10.1007/978-3-030-01264-9_45http://dx.doi.org/10.1007/978-3-030-01264-9_45］
Li C Y， Ma Q S， Yao H Y. 2006. Research on recognition of coarse-line damages based on borescope images. Chinese Journal of Scientific Instrument， 27（S3）： 2136-2137
李长有， 马齐爽， 姚红宇. 2006. 基于孔探图像的粗糙线条类损伤的识别研究. 仪器仪表学报， 27（S3）： 2136-2137 ［DOI： 10.3321/j.issn：0254-3087.2006.z3.147http://dx.doi.org/10.3321/j.issn：0254-3087.2006.z3.147］
Li H， Chen G， Chen X B and Wang H M. 2016. Study on automatic measurement method for aero-engine inner damage crack. Computer Engineering and Applications， 52（11）： 233-237
李华， 陈果， 陈新波， 王洪民. 2016. 航空发动机内部裂纹自动测量方法研究. 计算机工程与应用， 52（11）： 233-237 ［DOI： 10.3778/j.issn.1002-8331.1407-0423http://dx.doi.org/10.3778/j.issn.1002-8331.1407-0423］
Li Y D， Han Z Q， Xu H Y， Liu L Z， Li X Q and Zhang K K. 2019. YOLOv3-lite： a lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Applied Sciences， 9（18）： #3781 ［DOI： 10.3390/app9183781http://dx.doi.org/10.3390/app9183781］
Liu W， Anguelov D， Erhan D， Szegedy C， Reed S， Fu C Y and Berg A C. 2016. SSD： single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 21-37 ［DOI： 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2］
Long J， Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston， USA： IEEE： 3431-3440 ［DOI： 10.1109/CVPR.2015.7298965http://dx.doi.org/10.1109/CVPR.2015.7298965］
Maninis K K， Caelles S， Pont-Tuset J and Van Gool L. 2018. Deep extreme cut： from extreme points to object segmentation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 616-625 ［DOI： 10.1109/CVPR.2018.00071http://dx.doi.org/10.1109/CVPR.2018.00071］
Meng J R and Luo Y L. 2009. A damage assessment system for aero-engine borscopic inspection based on support vector machines//Proceedings of the 9th International Conference on Hybrid Intelligent Systems. Shenyang， China： IEEE： 3-8 ［DOI： 10.1109/HIS.2009.113http://dx.doi.org/10.1109/HIS.2009.113］
Pinheiro P O， Collobert R and Dollr P. 2015. Learning to segment object candidates//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal， Canada： MIT Press： 1990-1998
Pinheiro P O， Lin T Y， Collobert R and Dollr P. 2016. Learning to refine object segments//Proceedings of the 14th European Conference on Computer Vision. Amsterdam， the Netherlands： Springer： 75-91 ［DOI： 10.1007/978-3-319-46448-0_5http://dx.doi.org/10.1007/978-3-319-46448-0_5］
Redmon J， Divvala S， Girshick R and Farhadi A. 2016. You only look once： unified， real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 779-788 ［DOI： 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91］
Redmon J and Farhadi A. 2018. YOLOv3： an incremental improvement. ［EB/OL］. ［2022-07-08］. http：//arxiv.org/pdf/1804.02767.pdfhttp://arxiv.org/pdf/1804.02767.pdf
Ren S Q， He K M， Girshick R and Sun J. 2017. Faster R-CNN： towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（6）： 1137-1149 ［DOI： 10.1109/TPAMI.2016.2577031http://dx.doi.org/10.1109/TPAMI.2016.2577031］
Shen Z J， Wan X L， Ye F， Guan X J and Liu S W. 2019. Deep learning based framework for automatic damage detection in aircraft engine borescope inspection//Proceedings of 2019 International Conference on Computing， Networking and Communications （ICNC）. Honolulu， USA： IEEE： 1005-1010 ［DOI： 10.1109/ICCNC.2019.8685593http://dx.doi.org/10.1109/ICCNC.2019.8685593］
Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition//Proceedings of the 3rd International Conference on Learning Representations. San Diego， USA： ICLR
Svensén M， Hardwick D and Powrie H. 2018. Deep neural networks analysis of borescope images//Proceedings of the European Conference， of the PHM Society. Utrecht， The Netherlands： ［s.n.］
Tian Z， Shen C H， Chen H and He T. 2019. FCOS： fully convolutional one-stage object detection//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. Seoul， Korea （South）： IEEE： 9626-9635 ［DOI： 10.1109/ICCV.2019.00972http://dx.doi.org/10.1109/ICCV.2019.00972］
Wang C Y， Liao H Y M， Wu Y H， Chen P Y， Hsieh J W and Yeh I H. 2020. CSPNet： a new backbone that can enhance learning capability of CNN//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle， USA： IEEE： 1571-1580 ［DOI： 10.1109/CVPRW50498.2020.00203http://dx.doi.org/10.1109/CVPRW50498.2020.00203］
Wong C Y， Seshadri P and Parks G T. 2021. Automatic borescope damage assessments for gas turbine blades via deep learning//American Institute of Aeronautics and Astronautics （AIAA） Scitech 2021 Forum ［DOI： 10.2514/6.2021-1488http://dx.doi.org/10.2514/6.2021-1488］
Zhang H， Wu C R， Zhang Z Y， Zhu Y， Lin H B， Sun Y， He T， Mueller J， Manmatha R， Li M and Smola A. 2022. ResNeSt： split-attention networks//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. New Orleans， USA： IEEE： 2735-2745 ［DOI： 10.1109/CVPRW56347.2022.00309http://dx.doi.org/10.1109/CVPRW56347.2022.00309］
Zhang Y. 2004. The importance of borescope inspection in aircraft engine maintenance. Aviation Maintenance & Engineering， （1）： 24-25
张勇. 2004. 孔探工作在发动机维护中的重要性. 航空维修与工程， （1）： 24-25 ［DOI： 10.19302/j.cnki.1672-0989.2004.01.009http://dx.doi.org/10.19302/j.cnki.1672-0989.2004.01.009］
Zhang Y， Liu G J and Qiu J. 2008. Aeroengine’s fault detection technology based on image automatic measurement. Mechanical Science and Technology for Aerospace Engineering， 27（2）： 176-179
张勇， 刘冠军， 邱静. 2008. 基于图像自动测量的航空发动机故障检测技术研究. 机械科学与技术， 27（2）： 176-179 ［DOI： 10.3321/j.issn：1003-8728.2008.02.009http://dx.doi.org/10.3321/j.issn：1003-8728.2008.02.009］
Zhao Y Q， Rao Y， Dong S P and Zhang J Y. 2020. Survey on deep learning object detection. Journal of Image and Graphics， 25（4）： 629-654
赵永强， 饶元， 董世鹏， 张君毅. 2020. 深度学习目标检测方法综述. 中国图象图形学报， 25（4）： 629-654 ［DOI： 10.11834/jig.190307http://dx.doi.org/10.11834/jig.190307］
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