Yolov3-lite: A lightweight crack detection network for aircraft structure based on depthwise separable convolutions
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Due to the high proportion of aircraft faults caused by cracks in aircraft structures, crack inspection in aircraft structures has long played an important role in the aviation industry. The existing approaches, however, are time-consuming or have poor accuracy, given the complex background of aircraft structure images. In order to solve these problems, we propose the YOLOv3-Lite method, which combines depthwise separable convolution, feature pyramids, and YOLOv3. Depthwise separable convolution is employed to design the backbone network for reducing parameters and for extracting crack features effectively. Then, the feature pyramid joins together low-resolution, semantically strong features at a high-resolution for obtaining rich semantics. Finally, YOLOv3 is used for the bounding box regression. YOLOv3-Lite is a fast and accurate crack detection method, which can be used on aircraft structure such as fuselage or engine blades. The result shows that, with almost no loss of detection accuracy, the speed of YOLOv3-Lite is 50% more than that of YOLOv3. It can be concluded that YOLOv3-Lite can reach state-of-the-art performance.
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A lightweight YOLOv3-Lite combined with depthwise separable convolution and feature pyramids obtained 50% better speed than basic YOLOv3 on aircraft structure crack detection.
“They evaluated the method on fuselage, wing, aircraft tail or engine blades and they obtained 50% better speed than the basic YOLOv3 [31].”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [38]
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Finetuned YOLOv3 for Getting Four Times the Detection Speed
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- Authors
- Li, Y., Han, Z., Xu, H., Liu, L., Li, X., & Zhang, K.
- Affiliations
- Shanghai University (CN)
- Chinese Academy of Sciences (CN)
- Lenovo (China) (CN)
- Shanghai Micro Satellite Engineering Center (CN)
- Corresponding author
- Han, Zhenqi
- Liu, Lizhuang
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2019-09-10
- Volume
- 9
- Issue
- 18
- Pages
- 3781
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2972309292
- Cited by
- 115
- References
- 26
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 30 · 2023: 23 · 2022: 19 · 2021: 20 · 2020: 4 · 2019: 1
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Aerospace System Department (30508020301)
- Science and Technology Commission of Shanghai Municipality (17511106400,17511106402)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- crack detection, aircraft structures, depthwise separable convolution, lightweight object detection, feature pyramid, aircraft inspection, engine blades, aircraft fuselage, bounding box regression, parameter reduction
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