Ponencias (Instituto de Ciencia de Materiales de Sevilla (ICMS) – CIC Cartuja)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/10966

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Examinando Ponencias (Instituto de Ciencia de Materiales de Sevilla (ICMS) – CIC Cartuja) por Materia "Monitoring systems"

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    On-site forest fire smoke detection by low-power autonomous vision sensor
    (2010) Fernández Berni, Jorge; Carmona Galán, Ricardo; Carranza González, Luis; Cano Rojas, Alberto; Martínez Carmona, Juan F.; Rodríguez Vázquez, Ángel Benito; Morillas Castillo, Sergio; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; Ministerio de Ciencia e Innovación (MICIN). España
    Early 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.