| Citation: | CAI Shuyu, YAN Ziyan. Research on small target damage detection of aero-engine based on improved YOLOv4[J]. Journal of Aerospace Power, 2023, 38（2）：445-452 doi: 10.13224/j.cnki.jasp.20220557
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Intelligent aero-engines damage detection is an important research direction in aircraft fault diagnosis. An improved multi-scale target detection method based on You Only Look Once version 4 (YOLOv4) was proposed for the problem that existing target detection model has a poor effect on the detection of small target damage of aero-engine. A new shallow feature fusion layer was constructed in path aggregation network （PANet）, which fused shallower features with deep features to improve the network detection performance for small target damage. In order to reduce redundant parameters in the network, depthwise separable convolution was introduced in neck and the standard convolution was reconstructed into the form of depthwise separable convolution. Experiments showed that the improved YOLOv4 improved the detection accuracy of small target damage by 3.43%, reduced the model size by 54.06 MB, and increased the detection speed of the model by 31.03%. The results of the study indicated that the improved YOLOv4 model had better detection performance for small target damage.
| [1] |
张勇. 航空发动机故障诊断中孔探图像特征提取技术应用研究[D]. 长沙: 国防科技大学, 2006.
ZHANG Yong. Research on the application of endoscopic image feature extraction technology in aeroengine diagnosis[D]. Changsha: National University of Defense Technology, 2006. (in Chinese)
|
| [2] |
LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553): 436-444. doi: 10.1038/nature14539
|
| [3] |
JANG J,AN H,LEE J H,et al. Construction of faster R-CNN deep learning model for surface damage detection of blade systems[J]. Journal of the Korea Institute for Structural Maintenance and Inspection,2019,23(7): 80-86.
|
| [4] |
李龙浦. 基于孔探数据的航空发动机叶片损伤识别研究[D]. 天津: 中国民航大学, 2020.
LI Longpu. Research on damage identification of aeroengine blades based on borescope data[D]. Tianjin: Civil Aviation University of China, 2020. (in Chinese)
|
| [5] |
李浩. 基于图像识别的航空发动机叶片裂纹检测研究[D]. 成都: 电子科技大学, 2019.
LI Hao. Research on the blade crack detection of aero-engine based on image recognition[D]. Chengdu: University of Electronic Science and Technology of China, 2019. (in Chinese)
|
| [6] |
CHEN Z H, JUANG J C. Attention-based YOLOv4 algorithm in non-destructive radiographic testing for civic aviation maintenance[EB/OL]. [2022-12-21]. https://www.preprints.org/manuscript/202104.0653/v1.
|
| [7] |
陈为,梁晨红. 基于改进SSD的航空发动机目标缺陷检测[J]. 控制工程,2021,28(12): 2329-2335. doi: 10.14107/j.cnki.kzgc.cpcc2019-063
CHEN Wei,LIANG Chenhong. Aeroengine target defect detection based on improved SSD[J]. Control Engineering of China,2021,28(12): 2329-2335. (in Chinese) doi: 10.14107/j.cnki.kzgc.cpcc2019-063
|
| [8] |
SUN Q S,ZENG S G,LIU Y,et al. A new method of feature fusion and its application in image recognition[J]. Pattern Recognition,2005,38(12): 2437-2448. doi: 10.1016/j.patcog.2004.12.013
|
| [9] |
ZHAO Q, SHENG T, WANG Y, et al. M2det: a single-shot object detector based on multi-level feature pyramid network[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Honolulu, US: AAAI Press, 2019: 9259-9266.
|
| [10] |
KIM S W, KOOK H K, SUN J Y, et al. Parallel feature pyramid network for object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 234-250.
|
| [11] |
张陶宁,陈恩庆,肖文福. 一种改进MobileNet_YOLOv3网络的快速目标检测方法[J]. 小型微型计算机系统,2021,42(5): 1008-1014. doi: 10.3969/j.issn.1000-1220.2021.05.018
ZHANG Taoning,CHEN Enqing,XIAO Wenfu. Fast target detection method for improving MobileNet_YOLOv3 network[J]. Journal of Chinese Computer Systems,2021,42(5): 1008-1014. (in Chinese) doi: 10.3969/j.issn.1000-1220.2021.05.018
|
| [12] |
尹山青. 面向工件缺陷的小目标检测方法研究与应用[D]. 北京: 华北电力大学, 2021.
YIN Shanqing. Research and application of small object detection method for workpiece defects[D]. Beijing: North China Electric Power University, 2021. (in Chinese)
|
| [13] |
ZENG N,WU P,WANG Z,et al. A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection[J]. IEEE Transactions on Instrumentation and Measurement,2022,71: 3507014.1-3507014.14.
|
| [14] |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[EB/OL]. [2022-12-21].https: //arxiv.org/abs/2004.10934.
|
| [15] |
WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, US: IEEE, 2020: 390-391.
|
| [16] |
HE K,ZHANG X,REN S,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9): 1904-1916. doi: 10.1109/TPAMI.2015.2389824
|
| [17] |
NEUBECK A, VAN GOOL L. Efficient non-maximum suppression[C]//18th International Conference on Pattern Recognition (ICPR'06). Hong Kong: IEEE, 2006: 850-855.
|
| [18] |
CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. [2022-12-21]. https: //arxiv.org/abs/1706.05587.
|
| [19] |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Cham, Switzerland: Springer, 2016: 21-37.
|
| [20] |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition. Honolulu, US: IEEE, 2017: 2117-2125.
|
| [21] |
HOWARD A G, ZHU M, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL]. [2022-12-21].https: //arxiv.org/abs/1704.04861.
|
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