Yolo-sd: simulated feature fusion for few-shot industrial defect detection based on YOLOv8 and stable diffusion
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Few-shot learning has emerged as a promising approach, enabling YOLO to operate effectively even with limited labeled data.
“Few-shot learning has emerged as a promising approach, enabling YOLO to operate effectively even with limited labeled data by leveraging transfer learning and meta-learning techniques [147].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [147]
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 1× · see citing works
“Early researchers utilized traditional image processing methods to extract defect features, such as Histogram of Oriented Gradients (HOG) [23], Scale-Invariant Feature Transform (SIFT) [24], and Speeded Up Robust Features (SURF) [25].”
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1× · see citing works
“Early researchers utilized traditional image processing methods to extract defect features, such as Histogram of Oriented Gradients (HOG) [23], Scale-Invariant Feature Transform (SIFT) [24], and Speeded Up Robust Features (SURF) [25].”
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547 · see citing works
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558 · see citing works
Informative knowledge distillation for image anomaly segmentation
Cao, Y., Wan, Q., Shen, W., & Gao, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108846 · Cited by 130 · see citing works
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Wen, Y., & Wang, L.
- Affiliations
- Taiyuan University of Technology (CN)
- Corresponding author
- Wang, Li
- ORCID
- Wang, Li
- Venue
- International Journal of Machine Learning and Cybernetics
- Published
- 2024-05-01
- Volume
- 15
- Issue
- 10
- Pages
- 4589-4601
- ISSN
- 1868-8071 (ISSN-L)
- 1868-808X
- OpenAlex
- W4396546099
- Cited by
- 25
- References
- 7
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 4
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Machine Learning and Cybernetics (publishedVersion)
- Funders
- Regional innovation and Development Joint Fund of NSFC (No.U22A20167)
- National Key Research and Development Program of China (No.2021YFB3300503)
- Topics
- 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
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- industrial defect detection, few-shot learning, YOLOv8, Stable Diffusion, controllable image generation, LoRa, ControlNet, attention-based feature fusion, steel defect detection, NEU-DET, data augmentation, lightweight object detection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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