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Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition

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Abstract

In public spaces, conducting security checks to detect concealed objects carried on the human body is crucial for enhancing global anti-terrorist measures. Terahertz imaging has recently played a pivotal role in concealed object detection. However, previous studies have faced significant challenges in achieving superior accuracy and performance. To address these issues, we propose a YOLOv5m model for detecting hidden objects beneath human clothing. We employ the CSPDarknet53 block to reduce noise and enhance discriminative power. Object location and size are identified using a PANet and the prediction head. To reduce computational complexity and obtain highly relevant features, we utilize multi-convolutional layers. Duplicate boxes are eliminated and high-quality bounding boxes are accurately detected using the NMS block. Hyper parameter tuning is performed using the Mutation Enabled Salp Swarm Algorithm, resulting in improved detection accuracy and reduced processing time. Our proposed model achieves impressive metrics, including a precision of 98.99%, recall of 97.80%, F1 score of 98.05%, detection rate of 96.50% and execution time of 135 s. Comparatively, our method outperforms existing approaches such as CNN, YOLO3, AC-SDBSCAN, YOLO-v2, RaadNet and SPFAN. We train and test our proposed method using a terahertz video dataset, demonstrating excellent results with high precision.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Liang D, Xue F, Li L (2021) Active terahertz imaging dataset for concealed object detection. arXiv preprint arXiv:2105:03677

  2. Amadi L, Agam G (2023) Weakly Supervised 2D pose adaptation and body part segmentation for concealed object detection. Sensors 23(4):2005

    Article  Google Scholar 

  3. Liu T, Zhao Y, Wei Y, Zhao Y, Wei S (2019) Concealed object detection for activate millimeter wave image. IEEE Trans Industr Electron 66(12):9909–9917

    Article  Google Scholar 

  4. Cheng L, Ji Y, Li C, Liu X, Fang G (2022) Improved SSD network for fast concealed object detection and recognition in passive terahertz security images. Sci Rep 12(1):1–16

    Article  Google Scholar 

  5. Morozov AA, Sushkova OS (2021) Development of a publicly available terahertz video dataset and a software platform for experimenting with the intelligent terahertz visual surveillance. In: Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2020, Springer Singapore, p 105–113

  6. Feng H, An D, Tu H, Bu W, Wang W, Zhang Y, Zhang H, Meng X, Wei W, Gao B, Wu S (2020) A passive video-rate terahertz human body imager with real-time calibration for security applications. Appl Phys B 126(8):143

    Article  Google Scholar 

  7. Yağ İ, Altan A (2022) Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology 11(12):1732

    Article  Google Scholar 

  8. Sezer A, Altan A (2021) Detection of solder paste defects with an optimization-based deep learning model using image processing techniques. Solder Surf Mt Technol 33(5):291–298

    Article  Google Scholar 

  9. Ozcelik YB, Altan (2023) A classification of diabetic retinopathy by machine learning algorithm using entorpy-based features p 523–535.

  10. Altan A, Karasu S (2020) Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos, Solitons Fractals 140:110071

    Article  MathSciNet  Google Scholar 

  11. Wang W, Pei Y, Wang SH, manuel Gorrz J, Zhang YD (2023). PSTCNN: explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. Biocell: official journal of the Sociedades Latinoamericanas de Microscopia Electronica. vol 47(2), p 373

  12. Zhang Y, Deng L, Zhu H, Wang W, Ren Z, Zhou Q, Lu S, Sun S, Zhu Z, Gorriz JM, Wang S (2023) Deep learning in food category recognition. Inf Fus 95:101859

    Article  Google Scholar 

  13. Wang W, Zhang X, Wang SH, Zhang YD (2022) Covid-19 diagnosis by WE-SAJ. Syst Sci Control Eng 10(1):325–335

    Article  Google Scholar 

  14. Liu Y, Xu F, Pu Z, Huang X, Chen J, Shao S (2022) AC-SDBSCAN: toward concealed object detection of passive terahertz images. IET Image Proc 16(3):839–851

    Article  Google Scholar 

  15. Kowalski M (2019) Hidden object detection and recognition in passive terahertz and mid-wavelength infrared. J Infrared, Millim, Terahertz Waves 40(11–12):1074–1091

    Article  Google Scholar 

  16. Wang C, Shi J, Zhou Z, Li L, Zhou Y, Yang X (2020) Concealed object detection for millimeter-wave images with normalized accumulation map. IEEE Sens J 21(5):6468–6475

    Article  Google Scholar 

  17. Wang X, Gou S, Li J, Zhao Y, Liu Z, Jiao C, Mao S (2021) Self-paced feature attention fusion network for concealed object detection in millimeter-wave image. IEEE Trans Circuits Syst Video Technol 32(1):224–239

    Article  Google Scholar 

  18. Yang X, Wu T, Zhang L, Yang D, Wang N, Song B, Gao X (2019) CNN with spatio-temporal information for fast suspicious object detection and recognition in THz security images. Signal Process 160:202–214

    Article  Google Scholar 

  19. Pang L, Liu H, Chen Y, Miao J (2020) Real-time concealed object detection from passive millimeter wave images based on the YOLOv3 algorithm. Sensors 20(6):1678

    Article  Google Scholar 

  20. Amadi L, Agam G (2022) 2D-pose based human body segmentation for weakly-supervised concealed object detection in backscatter millimeter-wave images. In: Proceedings of the 26th International Conference of Pattern Recognition Systems (T-CAP@ ICPR 2022), Montreal, QC, Canada, p 21–25

  21. Gautam KS, Thangavel SK (2020) Hidden object detection for classification of threat. Int J Comput Aided Eng Technol 13(1–2):217–238

    Article  Google Scholar 

  22. Wang C, Yang K, Sun X (2020) Precise localization of concealed objects in millimeter-wave images via semantic segmentation. IEEE Access 8:121246–121256

    Article  Google Scholar 

  23. Wen Z, Yu K, Qi X, Sato T, Myint SH, Tamesue K, Katsuyama Y, Dobashi H, Murakami Y, Koyama I, Tokuda K (2022) AI-based W-band suspicious object detection system for moving persons two-stage walkthrough configuration and recognition optimization. Wirel Commun Mob Comput. https://doi.org/10.1155/2022/3690403

    Article  Google Scholar 

  24. Guo C, Hu F, Hu Y (2023) Concealed object detection for passive millimeter-wave security imaging based on task-aligned detection transformer. IEEE Trans Instrum Meas. https://doi.org/10.48550/arXiv.2212.00313

    Article  Google Scholar 

  25. Veranyurt O, Sakar CO (2023) Concealed pistol detection from thermal images with deep neural networks. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-15358-1

    Article  Google Scholar 

  26. Su B, Yuan M (2023) Object recognition for millimeter wave MIMO-SAR images based on high-resolution feature recursive alignment fusion network. IEEE Sens J. https://doi.org/10.1109/JSEN.2023.3284480

    Article  Google Scholar 

  27. Liang D, Xue F, Li L (2021) Active terahertz imaging dataset for concealed object detection. arXiv preprint arXiv:2105.03677

  28. Wang C, Luo Q, Chen X, Yi B, Wang H (2021) Citrus recognition based on YOLOv4 neural network. In: Journal of PHYSICS: Conference Series, vol 1820, p 012163. IOP Publishing

  29. Wang S, Liu Q, Liu Y, Jia H, Abualigah L, Zheng R, Wu D (2021) A hybrid SSA and SMA with mutation opposition-based learning for constrained engineering problems. Comput Intell Neurosci. https://doi.org/10.1155/2021/6379469

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of IT, IFET College of Engineering, Villupuram, Tamil Nadu, India

    J. Jayachitra

  2. Department of CSE, National Institute of Technology Silchar, Silchar, Assam, India

    K. Suganya Devi

  3. Department of IT, Anna University Regional Campus, Coimbatore, India

    S. V. Manisekaran

  4. Department of CSE, Vignan Foundation for Science, Technology and Research, Vadlamudi, Andhrapradesh, India

    Satish Kumar Satti

Authors
  1. J. Jayachitra
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  2. K. Suganya Devi
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  3. S. V. Manisekaran
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  4. Satish Kumar Satti
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Contributions

All agreed on the content of the study. JJ, KSD, SVM and SKS collected all the data for analysis. JJ, KSD, SVM and SKS agreed on the methodology. JJ, KSD, SVM and SKS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to K. Suganya Devi.

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Jayachitra, J., Devi, K.S., Manisekaran, S.V. et al. Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition. J Supercomput 80, 8357–8382 (2024). https://doi.org/10.1007/s11227-023-05717-y

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  • DOI: https://doi.org/10.1007/s11227-023-05717-y

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