Object localization in handheld thermal images for fireground understanding

Florian Vandecasteele, Bart Merci, Azarakhsh Jalalvand, Steven Verstockt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Despite the broad application of the handheld thermal imaging cameras in firefighting, its usage is mostly limited to subjective interpretation by the person carrying the device. As remedies to overcome this limitation, object localization and classification mechanisms could assist the fireground understanding and help with the automated localization, characterization and spatio-temporal (spreading) analysis of the fire. An automated understanding of thermal images can enrich the conventional knowledge-based firefighting techniques by providing the information from the data and sensing-driven approaches. In this work, transfer learning is applied on multi-labeling convolutional neural network architectures for object localization and recognition in monocular visual, infrared and multispectral dynamic images. Furthermore, the possibility of analyzing fire scene images is studied and their current limitations are discussed. Finally, the understanding of the room configuration (i.e., objects location) for indoor localization in reduced visibility environments and the linking with Building Information Models (BIM) are investigated.

Original languageEnglish (US)
Title of host publicationThermosense
Subtitle of host publicationThermal Infrared Applications XXXIX
EditorsDouglas Burleigh, Paolo Bison
PublisherSPIE
ISBN (Electronic)9781510609297
DOIs
StatePublished - 2017
Externally publishedYes
EventThermosense: Thermal Infrared Applications XXXIX 2017 - Anaheim, United States
Duration: Apr 10 2017Apr 13 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10214
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceThermosense: Thermal Infrared Applications XXXIX 2017
Country/TerritoryUnited States
CityAnaheim
Period4/10/174/13/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • convolutional neural networks
  • object detection and recognition
  • Thermal fire analysis

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