Smoke　usually　refers　to　a　visible　aerosol　produced　by　fuel　combustion,　sometimes　commingled　with　carbon　and　sulfur　particles　because　of　incomplete　burning.　The　long-term　accumulation　of　smoke　aerosols　is　an　important　factor　in　the　formation　of　haze,　which　is　an　environmental　concern　in　many　countries.　Developing　methods　of　intelligent　smoke　detection　in　the　air　would　be　greatly　beneficial　for　such　applications　as　industrial　safety　monitoring　and　prevention　of　air　pollution　for　the　purpose　of　wholly　replacing　manual　processes.　In　this　paper,　we　concentrate　on　resolving　this　difficulty.　Unlike　existing　deep　learning　frameworks　which　lack　generalization　ability　because　they　were　developed　for　specific　data　instances,　our　proposed　model　aggregates　simple　deep　convolutional　neural　networks　but　attains　excellent　performance.　In　order　to　capture　the　different　aspects　of　smoke,　we　define　a　set　of　feature　maps　that　are　fed　to　a　set　of　subnetworks.　Each　subnetwork　is　independently　trained　to　deliver　good　detection　performance.　A　final　output　is　obtained　by　selectively　aggregating　the　subnetwork　responses　via　majority　voting.　In　the　experiment　we　conducted　on　two　newly　established　noisy　smoke　image　datasets　corrupted　by　compression,　our　proposed　model　achieves　a　very　high　consistency　beyond　97%　on　average　between　detection　results　and　human　judgements,　outperforming　other　state-of-the-art　smoke　detection　algorithms　based　on　deep　learning.