Gun and Knife Detection Based on Faster R-CNN for Video Surveillance

  • M. Milagro Fernandez-CarroblesEmail author
  • Oscar Deniz
  • Fernando Maroto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)


Public safety in public areas is nowadays one of the main concerns for governments and companies around the world. Video surveillance systems can take advantage from the emerging techniques of deep learning to improve their performance and accuracy detecting possible threats. This paper presents a system for gun and knife detection based on the Faster R-CNN methodology. Two approaches have been compared taking as CNN base a GoogleNet and a SqueezeNet architecture respectively. The best result for gun detection was obtained using a SqueezeNet architecture achieving a 85.44% \(AP_{50}\). For knife detection, the GoogleNet approach achieved a 46.68% \(AP_{50}\). Both results improve upon previous literature results evidencing the effectiveness of our detectors.


Object detection Guns Knives Video surveillance 



This work was partially funded by projects TIN2017-82113-C2-2-R by the Spanish Ministry of Economy and Business and SBPLY/17/180501/000543 by the Autonomous Government of Castilla-La Mancha and the ERDF.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Milagro Fernandez-Carrobles
    • 1
    Email author
  • Oscar Deniz
    • 1
  • Fernando Maroto
    • 1
  1. 1.ETSI Industriales, VISILABUniversity of Castilla-La ManchaCiudad RealSpain

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