Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
Source abstract
Abstract
Concealed object detection in millimeter wave (MMW) images has gained significant attention in the realm of public safety, primarily due to its distinctive advantages of non-hazardous and non-contact operation. However, this undertaking confronts substantial challenges in practical applications, owing to the inherent limitations of low imaging resolution, small concealed object size, intricate environmental noise, and the need for real-time performance. In this study, we propose Swin-YOLO, an innovative single-stage detection model built upon transformer layers. Our approach encompasses several key contributions. Firstly, the integration of Local Perception Swin Transform Layers (LPST Layers) enhanced the network’s capability to acquire contextual information and local awareness. Secondly, we introduced a novel feature fusion layer and a specialized prediction head for detecting small targets, effectively leveraging the network’s shallow feature information. Lastly, a coordinate attention (CA) module was seamlessly incorporated between the neck network and the detection head, augmenting the network’s sensitivity towards critical regions of small objects. To validate the efficacy and feasibility of our proposed method, we created a new MMW dataset containing a large number of small concealed objects and conducted comprehensive experiments to evaluate the effectiveness of overall and partial improvements, as well as computational efficiency. The results demonstrated a remarkable 4.7% improvement in the mean Average Precision (mAP) for Swin-YOLO compared with the YOLOv5 baseline. Moreover, when compared with other enhanced transformer-based models, Swin-YOLO exhibited a superior accuracy and the fastest inference speed. The proposed model showcases enhanced performance and holds promise for advancing the capabilities of real-world applications in public safety domains.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Terahertz technology and applications · Advanced SAR Imaging Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
Funding
National Natural Science Foundation of China · Inner Mongolia Autonomous Region 2022 Science and Technology Leading Talent Team Project Task Letter · Project Plan and Task Letter for Basic Research Business Expenses of Universities Directly under the Autonomous Region
References (50)
Show source-provided references
1. A novel method for 3-D millimeter-wave holographic reconstruction based on frequency interferometry techniques10.1109/TMTT.2017.2772862 ↗
2. Detection of metallic and nonmetallic concealed targets based on millimeter-wave inverse scattering approach10.1002/mmce.22290 ↗
3. Yeom, S., Lee, D.S., Son, J.Y., and Kim, S.H. (2010, January 18–19). Concealed object detection using passive millimeter wave imaging. Proceedings of the IEEE 2010 4th International Universal Communication Symposium, Beijing, China.10.1109/IUCS.2010.5666180 ↗
4. Automatic image segmentation for concealed object detection using the expectation-maximization algorithm10.1364/OE.18.010659 ↗
5. Pang, L., Liu, H., Chen, Y., and Miao, J. (2020). Real-time concealed object detection from passive millimeter wave images based on the YOLOv3 algorithm. Sensors, 20.10.3390/s20061678 ↗
6. Zhang, Y., Wu, C., Liu, X., Wang, L., Dai, C., Cui, J., Li, Y., and Kinar, N. (2022). The Development of Frequency Multipliers for Terahertz Remote Sensing System. Remote Sens., 14.10.3390/rs14102486 ↗
7. Yeom, S., and Lee, D.S. (2013, January 16–18). Multi-level segmentation for concealed object detection with multi-channel passive millimeter wave imaging. Proceedings of the 2013 International Conference on IT Convergence and Security (ICITCS), Macau, China.10.1109/ICITCS.2013.6717861 ↗
8. A novel deformable body partition model for MMW suspicious object detection and dynamic tracking10.1016/j.sigpro.2020.107627 ↗
9. Meng, Z., Zhang, M., and Wang, H. (2020). CNN with pose segmentation for suspicious object detection in MMW security images. Sensors, 20.10.3390/s20174974 ↗
10. Concealed Object Detection in Millimeter Wave Image Based on Global Correlation of Multi-level Features in Cross-section Sequence
11. Zhang, K.S. (2022). Detection of Contraband Based on Millimeter Wave Image. [Master’s Thesis, Hangzhou Dianzi University].
12. Ultralytics (2020, June 09). Yolov5. Available online: https://github.com/ultralytics/yolov5.
13. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
14. Girshick, R. (2015, January 11–14). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Berlin, Germany.10.1109/ICCV.2015.169 ↗
15. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
16. Faster r-cnn: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
17. Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Computer Vision—ECCV 2016, Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 11–14 October 2022, Springer International Publishing.10.1007/978-3-319-46493-0 ↗
18. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
19. Redmon, J., and Farhadi, A. (2017, January 21–27). Yolo9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
20. Joseph, R., and Ali, F. (2018). Yolov3: An incremental improvement. arXiv.
21. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
22. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
23. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–22). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
24. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv.
25. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.10.1109/CVPR52729.2023.00721 ↗
26. Ultralytics (2023, January 10). Yolov8. Available online: https://github.com/ultralytics/ultralytics.
27. Recurrent models of visual attention
28. Hu, J., Shen, L., and Sun, G. (2018, January 18–22). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
29. Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
30. Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18–22). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00813 ↗
31. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv.
32. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11–17). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00986 ↗
33. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2021, January 4–9). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
34. Detection and segmentation of concealed objects in terahertz images10.1109/TIP.2008.2006662 ↗
35. Concealed object recognition based on geometric feature descriptors
36. Real-time computational processing and implementation for concealed object detection10.1117/1.OE.51.7.071405 ↗
37. Concealed object detection for activate millimeter wave image10.1109/TIE.2019.2893843 ↗
38. Fast and accurate concealed dangerous object detection
39. Domain adaptive detection system for concealed objects using millimeter wave images10.1007/s00521-021-05847-5 ↗
40. A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network10.3390/rs13244978 ↗
41. Concealed object detection for millimeter-wave images with normalized accumulation map10.1109/JSEN.2020.3040354 ↗
42. Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14–19). Cspnet: A new backbone that can enhance learning capability of CNN. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
43. Sun, Z., Li, P., Meng, Q., Sun, Y., and Bi, Y. (2023). An Improved YOLOv5 Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sensing, 15.10.3390/rs15071796 ↗
44. Bao, W., Du, X., Wang, N., Yuan, M., and Yang, X. (2022). A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images. Remote Sensing, 14.10.3390/rs14205176 ↗
45. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-iouloss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
46. Hou, Q., Zhou, D., and Feng, J. (2021, January 20–25). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Con-ference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01350 ↗
47. Gong, H., Mu, T., Li, Q., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., and Li, H. (2022). Swin-Transformer-Enabled YOLOv5 with Attention Mechanism for Small Object Detection on Satellite Images. Remote Sens., 14.10.3390/rs14122861 ↗
48. Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 19–25). Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone- captured scenarios. Proceedings of the IEEE/CVF International Conference on Computer Vision, Nashville, TN, USA.10.1109/ICCVW54120.2021.00312 ↗
49. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
50. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C.L. (2014). European Conference on Computer Vision, Springer.