# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 5 Conclusions  
This paper presented Swin-YOLO, a novel MMW object detection algorithm that combines Local Perception Swin Transformer and YOLOv5 to address the characteristics of small target size and complex features in MMW concealed object images. Firstly, to capture shallow features, extra feature fusion layers were added to obtain more low-level feature information. An additional prediction head was also added for the detection of tiny objects. Secondly, Local Perception Swin Transformer Layers were introduced to the neck network to enhance the global contextual information acquisition ability and increase the contextual connections of feature maps, effectively improving the detection performance of MMW small concealed objects. Moreover, Coordinate Attention modules were added to each detection head, and the coordinate-based attention made the network focus on the key areas of the MMW images and improved the localization accuracy of small object detection. Finally, a bidirectional feature pyramid network was employed to improve the feature fusion network, better integrating the extracted shallow and deep semantic features. To validate the detection performance of the proposed detector, an MMW concealed object image dataset collected by experimental radar was created. The experimental results show that the _mAP_ value of the proposed method reached 92% on the above dataset, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **P** & **R** & _mAP_ \\ \hline YOLOv5 & 88.7 & 82 & 87.3 \\ YOLOv5 + P2 & 91.3 & 82.5 & 88 \\ YOLOv5 + P2 + CA & 89.5 & 84.5 & 89.7 \\ YOLOv5 + P2 + LPST & 90.9 & 85.9 & 90.8 \\ YOLOv5 + P2 + CA + Swin Transformer & 91.7 & 85.6 & 91.2 \\ YOLOv5 + P2 + CA + LPST & 91.4 & 87.1 & 91.8 \\ Swin-YOLO & 91.8 & 87.3 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results on the MMW dataset.  
outperformed the YOLOv5 baseline network. Moreover, comparison experiments with other Transformer multi-head networks showed that the proposed detector achieves a balance between real-time performance and detection accuracy and is more suitable for small object detection in MMW human concealed object images. Still, the data in this article was not collected under complex and dynamic conditions. Therefore, we are not confident that our proposed model is valid on this data. In future work, improving the detection performance of the model in complex environments is a key task. However, in order to address the limitations of computing resources in security inspection scenarios, it can be considered to design a more lightweight model to improve detection efficiency.  
Conceptualization, P.H., R.W. and Y.S.; methodology, R.W.; software, P.H. and R.W.; validation, R.W., Y.S. and W.T.; formal analysis, Y.S.; investigation, R.W.; resources, P.H.; data curation, P.H.; writing--original draft preparation, P.H., R.W. and Y.S.; writing--review and editing, R.W., Y.S. and W.T.; visualization, R.W.; supervision, Y.S. and P.H.; project administration, W.T.; funding acquisition, P.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61971246), Inner Mongolia Autonomous Region 2022 Science and Technology Leading Talent Team Project Task Letter (Grant No. 2022 JJRC0002), Project Plan and Task Letter for Basic Research Business Expenses of Universities Directly under the Autonomous Region (Grant No. JY20220077).  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] from OBE Terahertz Science and Technology (Beijing) Co., Ltd. (Beijing, China) for the kind support with data collecting and labeling. The authors appreciate that the anonymous reviewers put forward their valuable comments and suggestions.  
The authors declare no conflict of interest.