# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 4 Experiments
### Experimental Results  
To validate the effectiveness of the proposed method, we conducted experiments with Swin-YOLO on the MMW dataset proposed in Section 4.1 and compared the results with other YOLO series models such as YOLOv8 [26], TPH-YOLOv5 [48], and STPH-YOLOv5. STPH-YOLOv5 combined the Swin Transformer with the C3 module based on the idea of C3 TR in TPH-YOLOv5. As shown in Table 3, our proposed method achieved a \(mAP\) of 92% on the MMW dataset, which is 4.7% higher than the YOLOv5 baseline method, 1.3% higher than that of the multi-head Transformer network, and 1.5% higher than that of the C3 STR network improved by the Swin Transformer. Moreover, the \(mAP\) of Swin-YOLO is 3.6% higher than most advanced single-stage detector YOLOv8 and 2.9% higher than two-stage detector Faster-RCNN. These results demonstrate the effectiveness of Swin-YOLO in detecting small concealed objects with complex features in MMW images. Figure 10 shows the \(P\)-\(R\) curves for each class of Swin-YOLO, which can reflect the relationship between precision and recall. For each class, the \(AP\) value is represented in the figure as the area of the \(P\)-\(R\) curve, and the results are shown in the legend of the figure. A higher \(AP\) value indicates the better detection performance of the model. From Figure 10, it can be seen that the improved model achieved good detection results for each class of objects. The class Cigarettes performed best with an accuracy of 96.1%, Lighters with an accuracy of 91.9%, Matches with an accuracy of 88.8%, Telephones with an accuracy of 95.1%, and Objects with an accuracy of 88%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(P\) & \(R\) & \(mAP\) \\ \hline YOLOv5 & 88.7 & 82 & 87.3 \\ YOLOv8 & 91.5 & 82.3 & 88.4 \\ Faster-RCNN & 90.4 & 83.8 & 89.1 \\ TPH-YOLOv5 & 90.6 & 86.1 & 90.7 \\ STPH-YOLOv5 & 90.3 & 85.8 & 90.5 \\ Swin-YOLO & 91.8 & 87.3 & 92.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results on the MMW dataset for different detection models. The intersection-over-union (IoU) threshold and confidence threshold are two fundamental measures of deep learning models. The confusion matrix of Swin-YOLO on the MMW dataset was calculated, as shown in Figure 11. We used an IoU threshold of 0.5 and a confidence threshold of 0.25, and visualized the classification of each class. In the confusion matrix plot, each row represents the predicted class, each column represents the actual class, and the data on the diagonal represents the proportion of correctly classified classes. As shown in Figure 11, most objects in the five classes were correctly predicted, indicating that the model has good performance. However, there is a high probability that the Object and Match classes are identified as background. For the class Object, the large number of objects and numerous object features can easily lead to misjudgments by the model. As for the class Match, the limited feature extraction due to the relatively small number of training samples leads to a higher false negative rate and a lower \(AP\) value compared with other classes. For the class Lighter, some targets were also mistakenly identified as background due to their extremely small size. Figure 12 shows the variation curves of loss values, including localization loss (Figure 12a), confidence loss (Figure 12b), and classification loss (Figure 12c). As shown from the figure, with the increase in the number of iterations, the three types of losses decrease steadily. The model converges after 50 iterations. The \(F1\) score curve shows the classification performance of the proposed model. As shown in Figure 13, Swin-YOLO has an effective classification effect on all classes, especially for class cigarette and class phone, followed by class lighter, class object and class match. Figure 10: P–R curve results of Swin-YOLO on MMW dataset.