Smoke　detection　plays　an　important　role　in　industrial　safety　warning　systems　and　fire　prevention.　Due　to　the　complicated　changes　in　the　shape,　texture,　and　color　of　smoke,　identifying　the　smoke　from　a　given　image　still　remains　a　substantial　challenge,　and　this　has　accordingly　aroused　a　considerable　amount　of　research　attention　recently.　To　address　the　problem,　we　devise　a　new　deep　dual-channel　neural　network　(DCNN)　for　smoke　detection.　In　contrast　to　popular　deep　convolutional　networks　(e.g.,　Alex-Net,　VGG-Net,　Res-Net,　and　Dense-Net　and　the　DNCNN　that　is　specifically　devoted　to　detecting　smoke),　our　proposed　end-to-end　network　is　mainly　composed　of　dual　channels　of　deep　subnetworks.　In　the　first　subnetwork,　we　sequentially　connect　multiple　convolutional　layers　and　max-pooling　layers.　Then,　we　selectively　append　the　batch　normalization　layer　to　each　convolutional　layer　for　overfitting　reduction　and　training　acceleration.　The　first　subnetwork　is　shown　to　be　good　at　extracting　the　detailed　information　of　smoke,　such　as　texture.　In　the　second　subnetwork,　in　addition　to　the　convolutional,　batch　normalization,　and　max-pooling　layers,　we　further　introduce　two　important　components.　One　is　the　skip　connection　for　avoiding　the　vanishing　gradient　and　improving　the　feature　propagation.　The　other　is　the　global　average　pooling　for　reducing　the　number　of　parameters　and　mitigating　the　overfitting　issue.　The　second　subnetwork　can　capture　the　base　information　of　smoke,　such　as　contours.　We　finally　deploy　a　concatenation　operation　to　combine　the　aforementioned　two　deep　subnetworks　to　complement　each　other.　Based　on　the　augmented　data　obtained　by　rotating　the　training　images,　our　proposed　DCNN　can　promptly　and　stably　converge　to　the　perfect　performance.　Experimental　results　conducted　on　the　publicly　available　smoke　detection　database　verify　that　the　proposed　DCNN　has　attained　a　very　high　detection　rate　that　exceeds　99.5　on　average,　superior　to　state-of-the-art　relevant　competitors.　Furthermore,　our　DCNN　only　employs　approximately　one-third　of　the　parameters　needed　by　the　comparatively　tested　deep　neural　networks.　The　source　code　of　DCNN　will　be　released　at　https://kegu.netlify.com/.