Ethylene　leakage　detection　has　become　one　of　the　most　important　research　directions　in　the　field　of　target　detection　since　ethylene　leakage　in　the　petrochemical　industry　is　closely　related　to　product　safety　and　environmental　pollution.　In　infrared　conditions,　many　factors　affect　the　texture　characteristics　of　ethylene,　such　as　ethylene　concentration,　background,　and　so　on.　We　find　that　the　detection　criteria　used　in　infrared　imaging　ethylene　leakage　detection　research　cannot　fully　reflect　real-world　production　conditions,　which　is　not　conducive　to　evaluating　the　performance　of　current　image-based　target　detection　methods.　Therefore,　we　create　a　new　infrared　image　dataset　of　ethylene　leakage　with　different　concentrations　and　backgrounds,　including　54275　images.　We　use　the　proposed　dataset　benchmark　to　evaluate　five　advanced　image-based　target　detection　algorithms.　Experimental　results　demonstrate　the　performance　and　limitations　of　existing　algorithms,　and　the　dataset　benchmark　has　good　versatility　and　effectiveness.　©　2023　IEEE.