Smoke　detection　plays　a　crucial　role　in　the　safety　production　of　petrochemical　enterprises　and　fire　prevention.　Image-based　machine　learning　and　deep　learning　methods　have　been　widely　studied.　Recently,　many　works　have　applied　the　transformer　to　solve　problems　faced　by　computer　vision　tasks　(such　as　classification　and　object　detection).　To　our　knowledge,　there　are　few　studies　using　the　transformer　structure　to　detect　smoke.　In　order　to　research　the　application　potential　and　improve　the　performance　of　the　transformer　in　the　smoke　detection　field,　we　propose　a　model　consisting　of　two　transformer　encoders　and　a　convolutional　neural　network　(CNN)　module.　The　first　transformer　encoder　can　be　used　to　establish　the　global　relationship　of　an　image,　and　the　CNN　structure　can　provide　additional　local　information　to　the　transformer.　The　fusion　of　global　information　and　local　information　is　conducive　to　the　second　transfer　encoder　to　make　better　decisions.　Experiments　results　on　large-size　dataset　for　industrial　smoke　detection　illustrate　the　effectiveness　of　the　proposed　model.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.