FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios 事件

FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios arXiv:2605.24508v1 Announce Type: new 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 sem