The　flare　stack　is　a　typical　flare　gas　combustion　facility　used　to　guarantee　the　safe　production　of　petrochemical　plants,　refineries,　and　other　enterprises.　One　of　the　most　vital　problems　of　a　flare　stack　is　the　incomplete　combustion　of　flare　gas,　which　produces　a　large　amount　of　flare　soot　and,　thus,　endangers　the　atmosphere　and　human　health.　Hence,　an　effective　and　efficient　flare　soot　monitoring　system　that　has　important　guiding　significance　to　environmental　protection　is　strongly　required.　To　this　end,　we　devise　a　vision-based　monitor　of　flare　soot　(VMFS)　that　can　search　for　flare　soot　in　a　timely　way　and　ensure　the　full　combustion　of　flare　gas.　First,　the　proposed　VMFS　leverages　the　broadly　tuned　color　channel　to　recognize　a　flame　in　an　input　video　frame　since　the　flame　is　the　source　of　flare　soot　in　our　application.　Second,　our　monitor　incorporates　fast　saliency　detection　with　K-means　to　fix　the　position　of　the　flame.　Third,　we　take　the　flame　area　as　the　center　to　search　for　the　potential　flare　soot　region,　followed　by　identifying　the　flare　soot　based　on　the　background　color　channel.　The　results　of　experiments　on　multiple　video　sequences　collected　at　a　real　petrochemical　plant　reveal　that　the　proposed　VMFS　is　superior　to　state-ofthe-art　relevant　models　in　both　monitoring　performance　and　computational　efficiency.　The　implementation　code　will　soon　be　released　at　https://kegu.netlify.com/.