Skip to main content
SpringerLink
Log in

YOLO-FDD: efficient defect detection network of aircraft skin fastener

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Fasteners defects in aircraft skin can seriously threaten the operational safeness of the aircraft. Therefore, the periodic detection of defects on aircraft skin fasteners is necessary. Due to the small size of aircraft skin fastener defects, traditional defect detection algorithms have lower detection precision. Therefore, we propose YOLO-FDD, an effective network for detecting aircraft skin fastener defects. Firstly, four detection layers and residual spatial pyramid pooling (R-SPP) module are used in the network. Secondly, an attention fusion feature pyramid networks (AF-FPN) is designed to adaptively fuse features and purify semantic conflicts. In addition, the attention head with swin transformer block (STB) and coordinate attention (CA) is designed as the feature detection module to enhance the location precision of defects. Finally, a dataset comprising of four types of aircraft skin fastener defects is constructed. According to the experimental findings, YOLO-FDD achieved a mAP of 83.08%, and the detection speed is 30.44 FPS, the YOLO-FDD network has great potential for application in aircraft skin fastener defect detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Y Deng X Liu L Udpa 2012 Magneto-optic imaging for aircraft skins inspection: a probability of detection study of simulated and experimental image data IEEE T Reliab. 61 4 901 908

    Article  Google Scholar 

  2. JM Padiyar L Zanotti Fragonara I Petrunin J Raposo A Tsourdos I Gray S Farmaki D Exarchos TE Matikas KG Dassios 2021 Fast, accurate, and reliable detection of damage in aircraft composites by advanced synergistic infrared thermography and phased array techniques Appl. Sci. 11 6 2778

    Article  Google Scholar 

  3. DA Tibaduiza-Burgos MA Torres-Arredondo 2015 Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics Smart Mater. Struct. 24 8 85020

    Article  Google Scholar 

  4. S Farmaki DA Exarchos IK Tragazikis TE Matikas KG Dassios 2020 A novel infrared thermography sensing approach for rapid, quantitative assessment of damage in aircraft composites Sensors (Basel) 20 15 4113

    Article  Google Scholar 

  5. Underhill, P.R., Krause, T.W.: Crack Detection Around Raised Head Rivets in Aluminum Aircraft Structures. J Nondestruct. Eval., 40(4) (2021)

  6. Z Zhou W Zhao J Li K Song 2023 SPCNet: a strip pyramid ConvNeXt network for detection of road surface defects Signal Image Video Process https://doi.org/10.1007/s11760-023-02698-6

    Article  Google Scholar 

  7. AF Kamanli 2023 A novel multi-scale cross-patch attention with dilated convolution (MCPAD-UNET) for metallic surface defect detection Signal Image Video Process https://doi.org/10.1007/s11760-023-02745-2

    Article  Google Scholar 

  8. Q Zhou S Ding Y Feng G Qing J Hu 2022 Corrosion inspection and evaluation of crane metal structure based on UAV vision SIViP 16 6 1701 1709

    Article  Google Scholar 

  9. Y Wu Y Qin Y Qian F Guo 2021 Automatic detection of arbitrarily oriented fastener defect in high-speed railway Automat. Constr. 131 103913

    Article  Google Scholar 

  10. J Wang L Luo W Ye S Zhu 2020 A defect-detection method of split pins in the catenary fastening devices of high-speed railway based on deep learning IEEE T Instrum. Meas. 69 12 9517 9525

    Article  Google Scholar 

  11. J Zhong Z Liu Z Han Y Han W Zhang 2019 A CNN-based defect inspection method for catenary split pins in high-speed railway IEEE T Instrum. Meas. 68 8 2849 2860

    Article  Google Scholar 

  12. J Chen Z Liu H Wang A Nunez Z Han 2018 Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network IEEE T Instrum. Meas. 67 2 257 269

    Article  Google Scholar 

  13. Y Li Z Han H Xu L Liu X Li K Zhang 2019 YOLOv3-Lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions Appl. Sci. 9 18 3781

    Article  Google Scholar 

  14. NP Avdelidis A Tsourdos P Lafiosca R Plaster A Plaster M Droznika 2022 Defects recognition algorithm development from visual UAV inspections Sensors (Basel) 22 13 4682

    Article  Google Scholar 

  15. B Ramalingam V Manuel MR Elara A Vengadesh AK Lakshmanan M Ilyas TJY James 2019 Visual inspection of the aircraft surface using a teleoperated reconfigurable climbing robot and enhanced deep learning technique Int. J. Aerosp. Eng. 2019 1 14

    Article  Google Scholar 

  16. J Wu G Shi S Lu K Li Y Gui D Wu 2021 Intelligent damage detection method of aircraft skin using drone vision China Meas. Test 2021 119 126

    Google Scholar 

  17. Bouarfa, S., Doğru, A., Arizar, R., Aydoğan, R., Serafico, J.: Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask R-CNN. In: AIAA Scitech 2020 Forum

  18. M Ding B Wu J Xu AN Kasule H Zuo 2022 Visual inspection of aircraft skin: Automated pixel-level defect detection by instance segmentation Chin. J. Aeronaut. 35 10 254 264

    Article  Google Scholar 

  19. Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936–944 (2017)

  20. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognitionar (CVPR) (2018). https://arxiv.org/abs/1803.01534

  21. Tan, M., Pang, R., Le, Q.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10778–10787 (2020)

  22. Chen, K., Cao, Y., Loy, C., Lin, D., Feichtenhofer, C.: Feature pyramid grids (2020). https://arxiv.org/abs/2004.03580v

  23. C Zhao X Shu X Yan X Zuo F Zhu 2023 RDD-YOLO: a modified YOLO for detection of steel surface defects Measurement 214 112776

    Article  Google Scholar 

  24. Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., Cho, K.: Augmentation for small object detection (2019). https://arxiv.org/abs/1902.07296v1

  25. Chen, C., Liu, M., Tuzel, C.O., Xiao, J.: R-CNN for small object detection. In: 13th Asian Conference on Computer Vision (ACCV) (2017). https://doi.org/10.1007/978-3-319-54193-8_14

  26. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021, 2107–8430 (2021). https://arxiv.org/abs/2107.08430v2

  27. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 9992–10002 (2021)

  28. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13708–13717 (2021)

  29. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression (2019). https://arxiv.org/abs/1911.08287v1

  30. S Ren K He R Girshick J Sun 2017 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks IEEE T PATTERN ANAL 39 6 1137 1149

    Article  Google Scholar 

  31. TY Lin P Goyal R Girshick K He P Dollar 2020 Focal loss for dense object detection IEEE T Pattern Anal. 42 2 318 327

    Article  Google Scholar 

  32. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection (2019). https://arxiv.org/abs/1904.01355v3

  33. Wang, C., Bochkovskiy, A., Liao, H.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022). https://arxiv.org/abs/2207.02696

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61573185 and in part by the Hui Yan Action of China under Grant F331D60F.

Author information

Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Huipeng Li, Congqing Wang & Yang Liu

Authors
  1. Huipeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Experimental design, data collection, and analysis of results were performed by H, CWL, and YL. H and CW wrote and checked the manuscript.

Corresponding author

Correspondence to Congqing Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Wang, C. & Liu, Y. YOLO-FDD: efficient defect detection network of aircraft skin fastener. SIViP 18, 3197–3211 (2024). https://doi.org/10.1007/s11760-023-02983-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02983-4

Keywords

Search

Navigation