用生成模型扩充脏餐具数据，让小样本下识别更准
DTGen: Generative Diffusion-Based Few-Shot Data Augmentation for Fine-Grained Dirty Tableware Recognition
- 用扩散模型加LoRA实现高效领域定制化生成
- 在极少真实数据下合成高质量脏餐具图像，准确率显著提升
- 适合嵌入式设备部署，可联动清洗程序智能调控能耗
智能餐具清洁是食品安全与智能家居的关键应用，但现有方法受限于粗粒度分类和少样本数据稀缺，难以满足工业化需求。本文提出DTGen，一种基于生成式扩散模型的少样本数据增强方案，专为细粒度脏餐具识别设计。通过LoRA实现高效领域适配，利用结构化提示生成多样脏污图像，并采用基于CLIP的跨模态过滤保障数据质量。在极有限的真实少样本条件下，DTGen可合成近乎无限的高质量样本，显著提升分类器性能，支持细粒度脏餐具识别。此外，研究还提出轻量化部署策略，有望将DTGen应用于嵌入式洗碗机，集成至清洁流程中，智能调节能耗与洗涤剂用量。实验表明，DTGen不仅验证了生成式AI在少样本工业视觉中的价值，也为自动化餐具清洁与食品安全监控提供了可行落地路径。
原文摘要 · Abstract (English)
Intelligent tableware cleaning is a critical application in food safety and smart homes, but existing methods are limited by coarse-grained classification and scarcity of few-shot data, making it difficult to meet industrialization requirements. We propose DTGen, a few-shot data augmentation scheme based on generative diffusion models, specifically designed for fine-grained dirty tableware recognition. DTGen achieves efficient domain specialization through LoRA, generates diverse dirty images via structured prompts, and ensures data quality through CLIP-based cross-modal filtering. Under extremely limited real few-shot conditions, DTGen can synthesize virtually unlimited high-quality samples, significantly improving classifier performance and supporting fine-grained dirty tableware recognition. We further elaborate on lightweight deployment strategies, promising to transfer DTGen's benefits to embedded dishwashers and integrate with cleaning programs to intelligently regulate energy consumption and detergent usage. Research results demonstrate that DTGen not only validates the value of generative AI in few-shot industrial vision but also provides a feasible deployment path for automated tableware cleaning and food safety monitoring.
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