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<p>Security screening systems require reliable and real-time detection of threats in complex X-ray imagery and surrounding environments. Manual inspection of baggage images is often a overhead due to operator fatigue, cluttered objects and overlapping items. Recent advances in deep learning and intelligent sensing technologies provide opportunities for automated threat detection in such environments. In this work, we propose a multi-layer airport security framework integrating three complementary detection modules: YOLO-based X-ray prohibited-item detection, video anomaly detection for behavioural monitoring, and IoT-based environmental anomaly sensing. In addition to this, a blockchain-secured logging mechanism is incorporated to ensure tamper-proof storage of security events. The X-ray detection module employs YOLOv11-s trained on the CLCXray dataset to identify prohibited items in baggage. Behavioural anomalies in surveillance footage are detected using a 3D-CNN autoencoder, while environmental anomalies from IoT sensors are identified using an LSTM autoencoder. Outputs from these modules are integrated using a unified multimodal risk scoring mechanism. Experimental results demonstrate that YOLOv11-s achieves mAP<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(_{50-95}\)</EquationSource></InlineEquation> of 78.91% and YOLOv8-s achieves mAP<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(_{50-95}\)</EquationSource></InlineEquation> of 78.49% with strong detection reliability across varying confidence thresholds, both the models have comparable performance and either of it could be chosen. The IoT anomaly detection produces reconstruction errors in the range of 0.019–0.03, with anomalies identified when the error exceeds a threshold of 0.041 and for video anomaly its 0.0040–0.00685. The proposed multimodal fusion module achieves ROC-AUC score of 0.864, which demonstrated an improved detection reliability compared to individual modalities. Also, the framework achieves an average detection latency of 0.014 ms per event, while the blockchain logging module records security events with an average latency of 0.179 ms and supports up to 5723.47 transactions per second. These results demonstrate that the proposed framework provides an effective, scalable, and secure solution for automated airport security monitoring systems.</p>

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A multi-layer airport security framework using YOLO-based X-ray detection, video anomaly analysis, IoT sensor monitoring and blockchain logging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • T. Shobha,
  • R. Rashmi,
  • L. R. Chaitanya,
  • C. V. Medhini,
  • R. J. Chandana,
  • Sumukh P. Gowda

摘要

Security screening systems require reliable and real-time detection of threats in complex X-ray imagery and surrounding environments. Manual inspection of baggage images is often a overhead due to operator fatigue, cluttered objects and overlapping items. Recent advances in deep learning and intelligent sensing technologies provide opportunities for automated threat detection in such environments. In this work, we propose a multi-layer airport security framework integrating three complementary detection modules: YOLO-based X-ray prohibited-item detection, video anomaly detection for behavioural monitoring, and IoT-based environmental anomaly sensing. In addition to this, a blockchain-secured logging mechanism is incorporated to ensure tamper-proof storage of security events. The X-ray detection module employs YOLOv11-s trained on the CLCXray dataset to identify prohibited items in baggage. Behavioural anomalies in surveillance footage are detected using a 3D-CNN autoencoder, while environmental anomalies from IoT sensors are identified using an LSTM autoencoder. Outputs from these modules are integrated using a unified multimodal risk scoring mechanism. Experimental results demonstrate that YOLOv11-s achieves mAP\(_{50-95}\) of 78.91% and YOLOv8-s achieves mAP\(_{50-95}\) of 78.49% with strong detection reliability across varying confidence thresholds, both the models have comparable performance and either of it could be chosen. The IoT anomaly detection produces reconstruction errors in the range of 0.019–0.03, with anomalies identified when the error exceeds a threshold of 0.041 and for video anomaly its 0.0040–0.00685. The proposed multimodal fusion module achieves ROC-AUC score of 0.864, which demonstrated an improved detection reliability compared to individual modalities. Also, the framework achieves an average detection latency of 0.014 ms per event, while the blockchain logging module records security events with an average latency of 0.179 ms and supports up to 5723.47 transactions per second. These results demonstrate that the proposed framework provides an effective, scalable, and secure solution for automated airport security monitoring systems.