There　is　a　necessary　combustion　facility　called　the　flare　stack　to　ensure　production　safety　in　every　petrochemical　plant,　smelter,　and　refinery　all　over　the　world.　Because　of　the　incomplete　combustion　of　flare　gas,　the　flare　stack　discharges　a　large　amount　of　smoke　into　the　atmosphere　and　make　it　dirty.　The　air　pollution　is　becoming　more　and　more　serious　caused　worldwide　concern.　Hence,　there　is　in　desperate　need　of　an　efficient　and　available　smoke　detection　method　to　protect　the　atmosphere.　To　this　end,　we　present　a　novel　self-attention　weight　transformer　(SAWT)　that　can　detect　accurately　smoke,　then　guarantee　full　combustion　of　the　flare　stack.　First,　we　are　concerned　about　the　weight　relationship　between　channels　and　draw　on　the　self-attention　mechanism　in　the　MobileViT　block　structure　to　adaptively　adjust　channel-wise　feature　weight.　Second,　we　leverage　short　connections　to　thoroughly　learn　and　fuse　local　and　global　features.　Results　of　experiments　on　a　real　smoke　dataset　reveal　that　the　proposed　SAWT　achieves　superior　performance　to　the　popular　deep　CNNs　and　state-of-the-art　smoke　detection　algorithms.　©　2024　SPIE.