# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 4 Experiments
### Experimental Results
To evaluate the computational efficiency of the proposed method, we compared the inference time model size of the proposed detection model with baseline and the Transformer-based TPH-YOLOv5, as shown in Table 4. Swin-YOLO demonstrated a speed advantage over TPH-YOLOv5, which verifies the advantage of the Swin Transformer in saving more computational resources than the Transformer. However, despite the improved detection performance, compared with the baseline, Swin-YOLO is slower than YOLOv5, indicating that simplifying the model to improve inference speed is a future research direction. In addition, the model size of Swin-YOLO has increased, compared with the original YOLOv5, due to these improvement measures, but is smaller than TPH-YOLOv5. Figure 11: The confusion matrix from the Swin-YOLO results on the MMW dataset. Figure 12: The Loss variation curves from the Swin-YOLO results: (**a**) localization loss; (**b**) classification loss; (**c**) confidence loss. In addition to inference time and model size, we also calculated the computational complexity of the proposed model, represented by giga floating-point operations per second (GFLOPs), which can be used to measure the computational requirements of the network. Swin-YOLO requires 231.5 Gflops. To better address the limitations of computing resources, a lightweight version of the model will be one of our focuses in the future. We present the detection results of the proposed model on a laboratory MMW dataset in Figure 14. Swin-YOLO can detect small objects with complex features and is more suitable for the detection of small objects, especially for the small class Cigarette. The addition of the Swin Transformer enables the model to capture rich global contextual information and enhance feature representation through self-attention mechanisms, which demonstrates the assisting value of global contextual information in object detection. In addition, the attention mechanism can effectively focus on key object regions for objects with indistinct features and biased angles. Moreover, the improvement of the layer structure, connections, and feature fusion methods of the neck network can enrich feature information and efficiently fuse features. Therefore, Swin-YOLO can better utilize the complex low-level and high-level features, which facilitates the detection of small objects. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Model Size (MB)** & **Inference Time (Per Picture)** \\ \hline YOLOv5 & 92.8 & 10.6 ms \\ TPH-YOLOv5 & 121.8 & 20.6 ms \\ Swin-YOLO & 110.5 & 16.7 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of inference time of different models on the MMW dataset. Figure 13: \(F1\) curve of Swin-YOLO on MMW dataset. However, from the detection results, although most items have been well detected, there are slight differences in the performance of the model for items displayed in different directions of the same class, especially in asymmetric images. When carrying items on the side of the human body, the hidden object features are easily confused with the background, posing a challenge for model detection. Therefore, in future work, the detection of side-carried objects will be a key research topic. Moreover, data detection in more complex detection environments, such as areas affected by noise and densely populated areas, is also one of the key issues to consider in the future.