## 3 Results and Discussion
### Performance of Blob-Level Classification
#### 3.4.2 Results on Training Set with Large FMs  
Overall, the FM-blob detection performance of the developed Fusion model on the training set with large 5 \(\times\) 5 mm\({}^{2}\) FMs was higher than both the VNIR and SWIR models (Table 3). The detection rates for all three FM groups using the developed Fusion model were 100%, whereas the detection rates of the VNIR and SWIR models were 95-98%, 95-100%, 92% for metal, wood, and opaque polymer, respectively. The higher detection rate of the Fusion model was because the VNIR and SWIR models were complementary to each other in detecting FMs. For example, in the case of the polymer, the VNIR model missed most of the dark green nitrile rubber conveyor belt samples, whereas the SWIR model detected all of these samples. In the opposite case, the SWIR model missed most pieces of white nitrile rubber conveyor belt and black nitrile rubber glove, whereas the VNIR model more accurately detected these samples missed by the SWIR model. The F1 score (96%) and JAC (93%) of the Fusion model were higher than those of the VNIR and SWIR models. The F1 score result suggested that the precision and recall of the Fusion model was better balanced than the VNIR and SWIR models.  
The recall (sensitivity) of the Fusion model was 100% which was about 7% higher than the VNIR and SWIR models. The precisions were 98% (VNIR), 94% (SWIR), and 93% (Fusion). Most of the FFs observed from the SWIR model were linked to a connective tissue that showed a spectral pattern similar to white plastic samples. The FFs observed from the Fusion model were carryovers from FPs found by the VNIR and SWIR models.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data** & **Model** & **Metal** & **Wood** & **Polymer** \\ \hline Training & VNIR & 62.3\% (3725/5979) * & 63.9\% (2296/3592) & 71.3\% (22104/30994) \\ (5 \(\times\) 5 mm\({}^{2}\)) & SWIR & 71.5\% (3361/4702) & 80.5\% (2624/3259) & 79.5\% (20118/25295) \\ \hline Test & VNIR & 10\% (371/3718) & 10.2\% (159/1552) & 33.1\% (5186/15671) \\ (2 \(\times\) 2 mm\({}^{2}\)) & SWIR & 37.7\% (1119/2966) & 58.9\% (874/1485) & 50\% (6241/12492) \\ \hline \end{tabular}
* Numbers in (\(/\)) represent (correctly identified pixels/ROI pixels).  
\end{table}
Table 2: Foreign material detection rates (%) based on pixel-level classification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Model** & **Metal DR** & **Wood DR** & **Polymer DR** & **Mean DR** & **FP** & **Precision** & **Recall** & **F1 Score** & **JAC** \\ \hline VNIR & 39 1 (97.5\%) 2 & 19 (95\%) & 157 (92.4\%) & 95.0\% & 5 & 97.7\% & 93.5\% & 95.6\% & 91.5\% \\ SWIR & 38 (95\%) & 20 (100\%) & 157 (92.4\%) & 95.8\% & 12 & 94.3\% & 93.5\% & 94.1\% & 88.8\% \\ Fusion & 40 (100\%) & 20 (100\%) & 170 (100\%) & 100\% & 17 & 92.7\% & 100\% & 96.4\% & 93.1\% \\ \hline \end{tabular}  
* Integers in Metal, Wood, and Polymer are the numbers of correctly detected FM blobs. 2 Percentage number in each 0 represents a detection rate.  
\end{table}
Table 3: Performance of three foreign material blob detection models on the training set. DR refers to detection rate.  
An example of the classification images obtained from the three FM-blob detection models (Fusion, VNIR, and SWIR) on the training set is shown in Figure 7.