用少量标注数据实现真实场景下食品缺陷的高效检测
FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
- 提出BBoxMixUp与一致性校准伪标签方法，缓解小样本偏差
- 在48类缺陷、13种食物上达到领先性能，提升明显
- 适合工业质检场景中数据稀缺的缺陷检测任务
食品缺陷检测对自动化质量控制至关重要，但现有研究缺乏统一基准且面临数据稀缺问题。本文构建FDD-48数据集，涵盖13种食品类型和48类缺陷，在多样化真实场景下提供细粒度标注。为提升有限标注数据下的检测效果，提出FDDet半监督框架，包含两项核心组件：(1) BBoxMixUp，通过混合同类别缺陷区域减少虚假特征关联；(2) CGPC（一致性引导伪标签校准），基于样本内一致性筛选伪标签。实验表明，FDDet在FDD-48上显著优于主流检测器，验证了其在数据受限场景下的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。