Real-Time YOLOv8l Contraband Detection in X- Ray Security Screening with Automated Identification of Weapons and Hazardous Objects for Threat Alerting

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 03 | Page :
By

Vaibhav Singh,

Vivek Verma,

  1. Project research scientist, Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India
  2. Student, Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India

Abstract

A real-time contraband detection system based on YOLOv8l has been proposed for use in very high security installations, such as airports and public screening facilities. The contraband detection system has been trained with curated X-ray contraband datasets containing twelve different object types like firearms, knives, bullets, etc., with the datasets having been constructed to provide an equal number of examples for each object class to support robust detection. The methodology of building this automated object detection system includes the use of sophisticated data preprocessing, data augmentation, and the use of an anchorless detection framework provided by the YOLOv8l model, allowing for high accuracy via confidence thresholds and non-max suppression methods. In conjunction with the use of GPU acceleration for real-time inference, the system performs at high frame rates, making it appropriate for operational deployment in a security environment. The experimental evaluation results from the testing performed on the YOLOv8l based real-time contraband detection system demonstrated the performance success of the system as mAP@50 of 94.30%, indicating that the contraband detection system will produce very accurate detection results across all object classes. An audio alarm will be provided by the system to immediately alert security personnel to a detected high threat level item, thus improving the situational awareness for all security personnel engaged in contraband detection at security screening events. The results shown in this study validate that the proposed methodology will provide automated, accurate and efficient contraband detection in real-world security environments.

Keywords: Transfer Learning, Object Detection, Deep Feature Extraction, Pretrained Networks, Image Processing, YOLOv8l, Contraband Detection, X-Ray Images, Real-Time Detection

How to cite this article: Vaibhav Singh, Vivek Verma. Real-Time YOLOv8l Contraband Detection in X- Ray Security Screening with Automated Identification of Weapons and Hazardous Objects for Threat Alerting. Journal of Image Processing & Pattern Recognition Progress. 2026; 13(03):-.
How to cite this URL: Vaibhav Singh, Vivek Verma. Real-Time YOLOv8l Contraband Detection in X- Ray Security Screening with Automated Identification of Weapons and Hazardous Objects for Threat Alerting. Journal of Image Processing & Pattern Recognition Progress. 2026; 13(03):-. Available from: https://journals.stmjournals.com/joipprp/article=2026/view=260843

References

Ahead of Print Subscription Original Research
Volume 13
03
Received 22/07/2026
Accepted 08/09/2026
Published 27/10/2026
Publication Time 97 Days

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