## 4 Experiments
### Object Detection  
To demonstrate that SWIR image data can be used for object detection as well as classification, we trained the YOLO v7 model [34]. To adapt the YOLO model to use SWIR images, which is normally used with RGB data, we represented the selected three wavelengths as a three-channel image by putting 1095 nm, 1345 nm, and 1580 nm wavelengths into the R, G, and B channels. After training and running inference with the YOLO v7 model on the acquired data, we obtained a confusion matrix, as shown in Figure 14. We used precision, recall, and mAP (mean Average Precision) metrics to evaluate our model's results (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **f1-Score** & **Number of Data Points** \\ \hline Real banana & 0.52 & 0.98 & 0.68 & 3233 \\ Fake banana & 0.95 & 0.25 & 0.39 & 3973 \\ \hline Real carrot & 0.69 & 0.95 & 0.80 & 3059 \\ Fake carrot & 0.91 & 0.52 & 0.66 & 2770 \\ \hline Real orange & 0.85 & 0.58 & 0.69 & 3095 \\ Fake orange & 0.67 & 0.90 & 0.77 & 2958 \\ \hline Real bell pepper & 0.99 & 0.49 & 0.66 & 2741 \\ Fake bell pepper & 0.70 & 1.00 & 0.82 & 3295 \\ \hline \end{tabular}
\end{table}
Table 1: Per-object classification result using RGB camera.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **f1-Score** & **Number of Data Points** \\ \hline Real banana & 0.87 & 0.97 & 0.92 & 3233 \\ Fake banana & 0.97 & 0.88 & 0.93 & 3973 \\ \hline Real carrot & 1.00 & 0.98 & 0.99 & 3059 \\ Fake carrot & 0.98 & 1.00 & 0.99 & 2770 \\ \hline Real orange & 1.00 & 0.99 & 1.00 & 3095 \\ Fake orange & 0.99 & 1.00 & 1.00 & 2958 \\ \hline Real bell pepper & 1.00 & 0.99 & 1.00 & 2741 \\ Fake bell pepper & 0.99 & 1.00 & 1.00 & 3295 \\ \hline \end{tabular}
\end{table}
Table 2: Per-object classification result using SWIR camera.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Precision** & **Recall** & **mAP 0.5** & **mAP 0.9** \\ \hline Human & 0.987 & 0.987 & 0.98 & 0.852 \\ Banner & 0.998 & 1.00 & 1.00 & 0.701 \\ Cotton mannequin & 0.879 & 0.981 & 0.981 & 0.854 \\ Plastic mannequin & 1.00 & 0.976 & 0.998 & 0.875 \\ \hline \end{tabular}
\end{table}
Table 4: Object detection results using SWIR wavelengths.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Precision** & **Recall** & **f1-Score** & **Number of Data Points** \\ \hline Real banana & 0.87 & 0.97 & 0.92 & 3233 \\ Fake banana & 0.97 & 0.88 & 0.93 & 3973 \\ \hline Real carrot & 1.00 & 0.98 & 0.99 & 3059 \\ Fake carrot & 0.98 & 1.00 & 0.99 & 2770 \\ \hline Real orange & 1.00 & 0.99 & 1.00 & 3095 \\ Fake orange & 0.99 & 1.00 & 1.00 & 2958 \\ \hline Real bell pepper & 1.00 & 0.99 & 1.00 & 2741 \\ Fake bell pepper & 0.99 & 1.00 & 1.00 & 3295 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of RGB and SWIR classification accuracy.  
These results indicate that SWIR-range imaging is effective for the detection and distinguishing of objects in difficult environments with the help of deep learning methods. The dataset used in our experiments included objects with some visual differences and was acquired in a controlled environment, meaning that these objects could potentially be distinguished using conventional RGB images. However, when appearances are highly similar or objects are farther away, as in our classification dataset, distinguishing subtle differences becomes challenging with RGB images alone. Additionally, as the number of object types and environmental variability in the dataset increases, RGB imaging may struggle to differentiate materials effectively, unlike SWIR. Thus, we demonstrate that, while objects with similar appearances are indistinguishable in RGB, SWIR imaging enables their differentiation by capturing physical characteristics. Furthermore, these characteristics can be learned by object detection models, extending the utility of SWIR in complex detection tasks.