Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX Article Swipe
Chunguang Zhang
,
Donglin Xu
,
Lifang Zhang
,
Wu Deng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12122672
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12122672
During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12122672
- https://www.mdpi.com/2079-9292/12/12/2672/pdf?version=1686742256
- OA Status
- gold
- Cited By
- 26
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380995530
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380995530Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics12122672Digital Object Identifier
- Title
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Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOXWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-14Full publication date if available
- Authors
-
Chunguang Zhang, Donglin Xu, Lifang Zhang, Wu DengList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12122672Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/12/12/2672/pdf?version=1686742256Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/12/2672/pdf?version=1686742256Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Train, Feature (linguistics), Computer vision, Surface (topology), Image (mathematics), Road surface, Process (computing), Materials science, Mathematics, Operating system, Cartography, Geography, Linguistics, Composite material, Geometry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 14, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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