# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Comparison with State-of-the-Art Methods  
We compared our ZSFDet framework with several state-of-the-art methods for zero-shot detection:  
* **Standard object detectors**: Faster R-CNN [52], trained only on seen classes. * **Zero-shot learning methods adapted for detection**: ConSE [56], SYNC [57], and DeviSE [23]. * **Zero-shot detection methods**: DSES [8], SB [58], ZSD-YOLO [58], PL [10], and RRFS [29]. We compared our ZSFDet framework with several state-of-the-art methods for zero-shot detection. We evaluated it against standard object detectors such as Faster R-CNN [52] trained only on seen classes, which serves as a baseline for conventional detection approaches. We also included zero-shot learning methods adapted for detection, including ConSE [56], SYNC [57], and DeViSE [23], which represent earlier approaches to transferring knowledge between seen and unseen classes. Additionally, we compared our framework with dedicated zero-shot detection methods including DSES [8], SB [8], ZSD-YOLO [58], PL [10], and RRFS [29], which represent the current state-of-the-art in zero-shot detection. Table 4 presents the zero-shot detection results of the FSVH dataset. Our method significantly outperforms all baseline approaches in both ZSD and GZSD settings. Specifically, ZSFDet achieves 63.7% mAP in ZSD, surpassing the previous state-of-the-art method RRFS by 6.9 percentage points. In the more challenging GZSD setting, our method achieves a harmonic mean of 59.8%, demonstrating its ability to simultaneously detect both seen and unseen hazard classes effectively. Table 5 shows the class-wise average precision (AP) for the selected unseen hazard classes. Our method performs well across different categories of food safety hazards, with a particularly strong performance for visually distinctive hazards like \"mold growth\" and \"glass fragments\". The relatively lower performance for \"bacterial colonies\" and \"chemical residue\" can be attributed to their subtle visual appearances, which make them inherently more challenging to detect in a zero-shot setting. Figure 3 presents the confusion matrix for unseen classes in the GZSD setting, revealing specific misclassification patterns. The matrix shows that fungal contamination classes (Aspergillus Mold and Penicillium Mold) exhibit 23% mutual confusion due to similar fuzzy growth patterns. Glass Fragments achieved the highest correct classification rate at 91%, with minimal confusion with other classes due to its distinctive visual signature. Bacterial Colonies shows a confused distribution, most frequently misclassified as Surface Moisture (18%) and Light Discoloration (15%), reflecting the visual similarity of early-stage microbial growth. Chemical Residue demonstrates the most challenging detection pattern, with no dominant confusion but errors distributed across multiple classes, indicating its subtle and varied visual manifestations. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**ZSD**} & \multicolumn{3}{c}{**GZSD (mAP)**} & \multicolumn{3}{c}{**GZSD (Recall@100)**} \\  & & **S** & **U** & **HM** & **S** & **U** & **HM** \\ \hline Faster R-CNN [52] & - & 68.5 & - & - & 74.2 & - & - \\ ConSE [56] & 42.1 & 67.3 & 39.4 & 49.8 & 70.5 & 45.6 & 55.3 \\ SYNC [57] & 44.5 & 65.8 & 41.2 & 50.6 & 71.3 & 47.9 & 57.3 \\ DeViSE [23] & 46.2 & 64.9 & 43.1 & 51.8 & 68.7 & 48.3 & 56.7 \\ DSES [8] & 50.3 & 62.7 & 47.8 & 54.2 & 67.9 & 53.1 & 59.6 \\ SB [8] & 51.8 & 66.3 & 48.5 & 56.0 & 72.1 & 52.7 & 60.9 \\ ZSD-YOLO [58] & 53.4 & 63.5 & 50.1 & 56.0 & 70.8 & 54.3 & 61.5 \\ PL [10] & 54.9 & 67.1 & 51.6 & 58.4 & 72.6 & 56.8 & 63.7 \\ RRFS [29] & 56.8 & 68.3 & 52.7 & 59.5 & 73.5 & 58.4 & 65.1 \\ \hline ZSFDet (Ours) & **63.7** & **68.9** & **53.5** & **60.2** & **74.6** & **63.2** & **68.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ZSD and GZSD results of the FSVH dataset. We report mAP@50 (%) and Recall@100 (%) for all methods. S and U denote seen and unseen classes, and HM denotes the harmonic mean.