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Detection of smoke from videos captured by surveillance cameras in outdoor environments is one of the useful outcome of Internet of Things (IoT) applications. The potential benefit increases when deep learning (DL) architectures are involved. However, an inherent difficulty is to detect smoke while natural events like fog exists. The effectiveness of color spaces in detection performance has not yet fully evaluated in those architectures. Moreover, the energy and memory requirements of DLdoi:10.18100/ijamec.973440 fatcat:rg43johzwzcsbe3rpu6xloehtu