A Novel Weapon Detection Framework in High-Energy X-Ray Dual-Energy Images Based on Shape and Edge Features


Weapon detection is a vital need in dual-energy X-ray luggage inspection systems at security of airport and strategic places. In this paper, a novel weapon detection framework in high-energy images of X-ray dual-energy images based on shape and edge features is proposed. In this framework, image enhancement is carried out by two noise removal and histogram stretching operations. Also, Connected Component Analysis (CCA) is applied to detect weapon candidate regions. An optimum feature set such as Fourier descriptors and invariant moments features are selected by feature forward selection algorithm and used to classify the detected objects into weapon (illicit) and non-weapon (lawful) objects using Probabilistic Neural Network (PNN) classifier. The proposed framework is evaluated on a perfect database consisting of various weapons in different size, type and view and accuracy rate of 96.48% has obtained.

  • Abstract
  • Key Words
  • 1. Introduction
  • 2. The Proposed Weapon Detection Framework
  • 3. Probabilistic Neural Network Classifier
  • 4. Experimental Results
  • 5. Conclusions
  • Acknowledgment
  • Reference

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