Shallow buried improvised explosive device detection via convolutional neural networks

Simon Colreavy-Donnelly, Fabio Caraffini, Stefan Kuhn, Mario Gongora, Johana Florez-Lozano, Carlos Parra

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.

Original languageEnglish
Pages (from-to)403-416
Number of pages14
JournalIntegrated Computer-Aided Engineering
Volume27
Issue number4
DOIs
StatePublished - 2020

Keywords

  • Land mine detection
  • convolutional neural network
  • image processing
  • improvised explosive device
  • land sensing

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