2018-07-242018-07-242010Fernández Berni, J., Carmona Galán, R., Carranza González, L., Cano Rojas, A., Martínez Carmona, J.F., Rodríguez Vázquez, Á.B. y Morillas Castillo, S. (2010). On-site forest fire smoke detection by low-power autonomous vision sensor. En VI International Conference on Forest Fire Research, Coimbra (Portugal).https://hdl.handle.net/11441/77544Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby very early detection and precise location of forest fires. One of the fundamental requirements that need to be met at the network nodes is reliable low-power on-site image processing. It greatly simplifies the communication infrastructure of the network as only alarm signals instead of complete images are transmitted, anticipating thus a very competitive cost. As a first approximation to fulfill such a requirement, this paper reports the results achieved from field tests carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system, built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was triggered despite the significant motion other than smoke present in the scene. Finally, as a further step, we describe the preliminary laboratory results obtained from a prototype vision chip which implements, at very low energy cost, some image processing primitives oriented to environmental monitoring.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Forest firesMonitoring systemsWireless sensor networksAutomatic early detectionArtificial visionAutonomous sensorsVision algorithmsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess