# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 4 Experiments
### Dataset and Evaluation Metrics  
The MMW dataset used in our study was acquired by an experimental MMW radar system, which is illustrated in Figure 8. The experimental radar system generates multi-angle human images by scanning the entire 360-degree view.  
Figure 7: Improved multi-scale feature fusion network.  
This dataset mainly simulates the conditions for production units to inspect fire sources, electronic devices, and other suspicious items. For data collection, volunteers were requested to conceal and carry pre-arranged items, including cigarettes, lighters, matches, telephones, and other suspicious objects, on their limbs and anterior and posterior torso positions, after which the volunteers entered the MMW radar system for body scanning. The MMW scanner rotated a full circle to perform a body scan of the volunteer and generated multi-angle MMW images of the human body concealed objects, including 7 angles of the front and 7 angles of the back surface of the body. All objects in the images were independently labeled at different angles and all had different patterns, and the multi-angle images enabled the dataset to possess concealed object features that were visible at specific angles. Due to the relatively stable collection environment of this dataset, it was less affected by environmental noise. In terms of dataset size, the MMW dataset included a total of 82,014 MMW radar imaging pictures, with 266,109 manually labeled target labels among the 5 classes. Figure 9 presents the label counts for each class. The dataset is divided into training set, test set, and validation set in an 8:1:1 ratio.  
For image detection evaluation, we chose precision (_P_), recall (_R_), average precision (_AP_), and mean average precision (_mAP_) as evaluation metrics. True positive (_TP_), false positive (_FP_), true negative (_TN_), and false negative (_FN_) were used to define \(P\) and \(R\), as shown in Equations (13) and (14) below. If the Intersection over Union (IoU) threshold of the between the detected bounding box and the ground truth is greater than 0.5, the detection box is marked as _TP_; Otherwise, it is marked as _FP_. If a concealed object is not matched to any detection box, it is marked as _FN_. _TN_ is not considered in the binary classification problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **0-32 \(\times\) 32 Pixels** & **32 \(\times\) 32–64 \(\times\) 64 Pixels** & **>64 \(\times\) 64 Pixels** \\ \hline MMW Dataset & 66\% & 33\% & 1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The distributions of the instance size for the MMW dataset.  
Figure 8: Experimental MMW Radar System Model.  
In addition, we conducted a statistical analysis of the object size of the MMW dataset images, and objects with a pixel size smaller than 32 \(\times\) 32 were defined as small objects. As shown in Table 2, the number of small objects accounted for 66% of the dataset, while targets with pixel sizes ranging from 32 \(\times\) 32 to 64 \(\times\) 64 accounted for 33%. It was evident that the majority of concealed objects in the MMW dataset were small objects, which posed a certain challenge to the training of the YOLOv5 network.  
\[P=\frac{TP}{(TP+FP)}, \tag{13}\]  
\[R=\frac{TP}{(TP+TN)}, \tag{14}\]  
\(AP\) is the integral of the precision-recall curve (\(P\)-\(R\) curve) in the range of 0 to 1. A higher \(AP\) value indicates higher detection accuracy, as defined in Equation (15).  
\[AP=\int_{0}^{1}P(R)dR, \tag{15}\]  
In multi-class object detection tasks, the detection accuracy of a model is evaluated by calculating \(mAP\) (the average of \(APs\) of all classes) as shown in Equation (16) below, where \(N\) represents the number of classes:  
\[mAP=\frac{1}{N}\sum AP, \tag{16}\]  
\(F1\) score is the harmonic mean of \(P\) and \(R\), as expressed in Equation (17):  
\[F1=\frac{2\times P\times R}{P+R}, \tag{17}\]  
Figure 9: The distribution of the classes of the labels for the MMW dataset.