# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Discussion  
The experimental results demonstrate the effectiveness of our Zero-Shot Food Safety Hazard Detection framework in detecting previously unseen food safety hazards. The significant performance improvement compared to state-of-the-art methods can be attributed to three key factors. First, the Food Safety Knowledge Graph provides structured domain knowledge about the visual attributes of food safety hazards, enabling a more effective knowledge transfer from seen to unseen hazard classes. This domain-specific knowledge is particularly valuable in the food safety domain, where visual cues for safety hazards can be subtle and context-dependent. Unlike generic zero-shot detection methods such as RRFS [29] that rely solely on word embeddings, our approach encodes explicit relationships between food categories and hazard manifestations, resulting in 6.9% higher mAP on unseen classes. Second, the Multi-Source Graph Fusion module effectively integrates knowledge from multiple sources, creating rich semantic representations that capture complex relationships between food categories and safety attributes. This contrasts with previous knowledge-enhanced approaches like KEFS [12], which focuses on ingredient correlation for food recognition rather than safety hazard detection. While KEFS achieves a strong performance in food category detection, it lacks the fine-grained visual attribute modeling necessary for distinguishing subtle contamination patterns. Our multi-source fusion uniquely combines taxonomic knowledge (Hyperclass Graph), empirical patterns (Co-occurrence Graph), and domain expertise (FSKG), addressing the complex nature of food safety hazards. Third, RFDM generates more diverse and realistic visual features for unseen hazard classes compared to traditional GAN-based approaches. Recent work in zero-shot detection has shown that feature quality directly impacts detection performance [25], yet most methods struggle with mode collapse when synthesizing features for fine-grained classes. Our diffusion-based approach maintains feature diversity while ensuring class discriminability, which is crucial for distinguishing visually similar hazards. Our approach addresses critical limitations in existing food safety inspection systems. Traditional computer vision methods for food safety, such as hyperspectral imaging [4] and multispectral analysis [5], achieve high accuracy on known contaminants but require specialized hardware and cannot adapt to novel hazards. Deep learning approaches [14] have shown promise but require extensive labeled datasets for each hazard type--a significant limitation given the constantly evolving nature of food safety threats. Our zero-shot frame work bridges this gap by enabling the detection of emerging hazards without retraining, using only semantic descriptions and structured domain knowledge. Recent advances in vision-language models such as CLIP [60] and ALIGN [61] offer alternative approaches to zero-shot recognition. However, our experiments show that these models struggle with the fine-grained visual distinctions critical for food safety assessment. Generic vision-language pretraining lacks the domain-specific knowledge necessary to distinguish between benign surface variations and actual contamination. Our structured knowledge approach addresses this limitation by explicitly encoding relationships between visual attributes and safety hazards, achieving 18.4% higher accuracy than CLIP-based detection on food safety benchmarks. Despite these advances, several limitations warrant further investigation. The performance on certain hazard classes with extremely subtle visual cues (e.g., bacterial colonies, chemical residues) remains relatively low compared to more visually distinctive hazards. This suggests fundamental challenges in the visual detection of certain contamination types that may require complementary sensing modalities.