Abstract:Food defect detection is critical for automated quality control, yet existing studies lack unified benchmarks and suffer from data scarcity. We introduce FDD-48, a comprehensive dataset with fine-grained annotations across 13 food types and 48 defect categories under diverse real-world conditions. To improve detection with limited labeled data, we propose FDDet, a semi-supervised framework featuring two key components: (1) BBoxMixUp, a data augmentation technique that mixes same-category defect regions to reduce spurious feature associations, and (2) CGPC (Consistency-Guided Pseudo-Label Calibration), which filters pseudo-labels based on intra-sample consistency. Experiments show FDDet significantly outperforms mainstream detectors on FDD-48, demonstrating its effectiveness for food defect detection under data-limited scenarios.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.24508 [cs.CV] |
| (or arXiv:2605.24508v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24508 arXiv-issued DOI via DataCite (pending registration) |
From: Ruihao Xu [view email]
[v1]
Sat, 23 May 2026 10:42:21 UTC (5,851 KB)
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