Industrial　enterprises,　such　as　thermal　power　plants　and　petrochemical　factories,　use　the　flare　stack　to　eliminate　volatile　organic　compounds　(VOCs)　emerged　in　the　production　process.　Through　complete　burning　of　the　flare　gas,　the　flare　stack　manages　to　prevent　air　pollution　and　protect　the　safety　of　the　plants.　The　monitoring　of　the　flare　stack　is　of　great　importance　for　the　reason　that　uncomplete　combustion　will　endanger　the　environment　and　even　people＇s　lives.　In　this　study,　an　effective　and　intelligent　flare　soot　monitoring　system　has　been　designed　to　deal　with　this　situation.　Unlike　some　existing　work,　the　proposed　monitoring　system　tries　to　identify　combustion　status　of　the　flare　gas　through　both　RGB　image　and　thermal　infrared　image　(see　Fig.1).　The　new　designed　system　can　monitor　the　flare　soot　without　concerning　the　light　and　weather　conditions.　Further　more,　we　also　constructed　a　deep　neural　model　based　on　attention　mechanisms　and　meta-learning　to　cooperate　the　problem　of　lacking　sufficient　black　smoke　samples.　Experimental　results　showed　that　the　devised　monitoring　system　can　recognize　black　smoke　through　videos　of　flare　combustion　timely　and　accurately　regardless　of　the　light　and　weather.　©　2021　IEEE