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CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection — arXiv:2608.28070

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28070
License (as archived): CC0-1.0
Archived: 2026-10-17T11:33:07.063Z
SHA-256 of the archived text: 6f35da502c6fa226579621e7c3af5c9f3c75a1265807ef3ba988f05c3e8e2f24
Stable URL of this page: https://metraipolis.com/documento/dc_2133c76cdbdd

How to cite

CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection — arXiv:2608.28070 — arXiv (en), https://arxiv.org/abs/2608.28070 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2133c76cdbdd (Metraipolis archive dc_2133c76cdbdd, sha256 6f35da502c6fa226579621e7c3af5c9f3c75a1265807ef3ba988f05c3e8e2f24)

BibTeX

@misc{dc2133c76cdbdd,
  title = {CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection — arXiv:2608.28070},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28070},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2133c76cdbdd; sha256 6f35da502c6fa226579621e7c3af5c9f3c75a1265807ef3ba988f05c3e8e2f24},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28070. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automated detection of surface micro-defects on industrial components, such as copper tubes, is critically important for quality assurance but remains challenging due to the minute scale of anomalies and their visual camouflage against complex backgrounds. These factors lead to weak feature representations and high rates of false positives and missed detections. To address these issues, we propose a novel real-time detection framework designed for efficient context perception and feature refinement. Our method integrates a Context-Perception Aggregation Module (CPAM), which synergises large-kernel perception for macro-texture context and small-kernel aggregation for sharp boundary delineation, effectively breaking the background camouflage. Furthermore, a Feature Additive Refinement Module (FARM) employs a linear-complexity additive token mixer to globally verify and refine the representation of fine-grained anomalies, suppressing noise-induced errors. To support research in this domain, we introduce the Copper Tube Defect Dataset (CTDD), a manually annotated benchmark containing 1,847 images and 4,898 boundingbox defect instances from copper-tube inspection scenarios. Extensive experiments demonstrate that our detector achieves strong and consistent performance on CTDD, outperforming representative baseline detectors, including YOLOv11, by 2.2% in mAP@50 and 3.9% in Precision while maintaining real-time inference speed. This work provides a robust and efficient solution for high-precision industrial inspection, bridging the gap between contextual understanding and detailed feature analysis. Our code and model are available at: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-31 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.28070 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T11:10:05.297Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).