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.
Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.