# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Analysis of Visual Attribute Influence on Detection Performance  
To further understand how different visual attributes contribute to the detection performance, we conducted an in-depth analysis of the relationship between attribute types \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Inference Time (ms)** & **Model Size (MB)** \\ \hline Faster R-CNN [52] & 85 & 235 \\ SB [8] & 92 & 248 \\ ZSD-YOLO [58] & 45 & 240 \\ RRFS [29] & 103 & 276 \\ \hline ZSFDet (Ours) & 108 & 285 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational efficiency comparison. Inference time is measured in milliseconds per image on an NVIDIA Tesla V100 GPU. Note: Inference time measured on NVIDIA Tesla V100 GPU for single image processing. Model size includes all parameters and auxiliary data structures required for inference. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **MVTec-AD** & **Food-5K** \\ \hline ConSE [56] & 35.8 & 28.6 \\ SYNC [57] & 38.2 & 30.9 \\ DeViSE [23] & 39.5 & 31.4 \\ DSES [8] & 42.7 & 33.8 \\ SB [8] & 43.9 & 35.2 \\ ZSD-YOLO [58] & 45.3 & 36.7 \\ PL [10] & 46.8 & 38.1 \\ RRFS [29] & 48.5 & 39.4 \\ \hline ZSFDet (Ours) & **54.2** & **43.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross-dataset evaluation results. We report mAP@50 (%) for Zero-Shot Detection (ZSD). and detection accuracy across food categories. Figure 6 presents a comprehensive analysis of this relationship through multiple visualization techniques. The radar chart in Figure 6a reveals that our ZSFDet framework consistently outperforms baseline methods across all attribute categories, with particularly significant improvements for hazards characterized by texture and color attributes. For texture-based attributes, ZSFDet achieves a 67.8% mAP, compared to 57.2% and 51.5% for RRFS and SB, respectively. This substantial improvement can be attributed to the Multi-Source Graph Fusion module, which effectively captures complex texture patterns through the integration of multiple knowledge sources. Figure 6b presents a correlation matrix highlighting the relationship between different visual attribute types and detection performance across food categories. Strong positive correlations (0.82) were observed between color-based attributes and detection accuracy for fruits and vegetables, while texture-based attributes showed the highest correlation (0.76) for baked goods. Shape-based attributes demonstrated strong correlations (0.73) with detection performance for processed foods. These findings suggest that different food categories require different levels of attribute attention for optimal hazard detection. The attribute contribution analysis in Figure 6c quantifies the impact of each attribute type on detection accuracy through permutation feature importance. Texture attributes contribute most significantly to the detection of mold (31.5%), while color attributes are most important for detecting chemical residues (28.7%). For foreign objects, shape attributes dominate, with a 34.2% contribution. This analysis provides valuable insights for optimizing attribute selection in the knowledge graph for different hazard types. Figure 6: Analysis of visual attributes’ influence on detection performance. (**a**) Radar chart showing the detection performance (mAP@50) of different methods across five attribute categories. (**b**) Correlation matrix between visual attribute types and detection performance across food categories. (**c**) Attribute contribution analysis showing the impact of each attribute type on detection accuracy for seen and unseen hazards. (**d**) Hierarchical clustering of food safety hazards based on their visual attribute similarities and detection difficulty. Figure 6d presents a hierarchical clustering of food safety hazards based on their visual attribute similarities and detection difficulty.