Although　object　detection　algorithms　based　on　deep　learning　have　been　widely　used　in　many　scenarios,　they　face　challenges　under　some　degraded　conditions,　such　as　low-light.　A　conventional　solution　is　that　image　enhancement　approaches　are　used　as　a　separate　pre-processing　module　to　improve　the　quality　of　degraded　image.　However,　this　two-step　approach　makes　it　difficult　to　unify　the　goals　of　enhancement　and　detection,　that　is,　low-light　enhancement　operations　are　not　always　helpful　for　subsequent　object　detection.　Recently,　some　works　try　to　integrate　enhancement　and　detection　in　an　end-to-end　network,　but　still　suffer　from　complex　network　structure,　training　convergence　problem　and　demanding　reference　images.　To　address　above　problems,　a　plug-and-play　image　enhancement　model　is　proposed　in　this　paper,　namely,　low-light　image　enhancement　(LLIE)　model,　which　can　be　easily　embedded　into　some　off-the-shelf　object　detection　methods　in　an　end-to-end　manner.　LLIE　is　composed　of　a　parameter　estimation　module　and　image　processing　module.　The　former　learns　to　regress　lighting　enhancement　parameters　according　to　the　feedback　of　detection　network,　and　the　latter　enhances　degraded　image　adaptively　to　promote　subsequent　detection　model　under　low-light　condition.　Extensive　object　detection　experiments　on　several　low-light　image　data　sets　show　that　the　performance　of　detector　is　significantly　improved　when　LLIE　is　integrated.　©　2024,　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.