A　panchromatic　image　is　a　remote　sensing　image　that　is　imaged　at　the　entire　visible　light　band.　It　often　has　the　highest　spatial　resolution　and　is　widely　used　in　the　fields　of　resource　detection,　urban　planning,　geographic　information　systems,　military,　and　national　defense　etc.　However,　the　feature　of　single-band　imaging　determines　that　panchromatic　images　are　usually　displayed　in　the　form　of　grayscale　and　result　in　some　detailed　differences　between　distinct　types　of　ground　objects　that　are　indecipherable.　It　causes　many　difficulties　for　object　detection　applications.　To　address　this　problem,　an　object　detection-oriented　style　transfer　network　for　panchromatic　remote　sensing　image　is　designed.　In　full　consideration　of　the　actual　requirements　of　object　detection　tasks　for　panchromatic　remote　sensing　image,　a　style　transfer　network　based　on　feature　fusion　is　designed,　where　a　style　transfer　model　is　trained　to　transfer　the　grayscale　panchromatic　image　into　the　corresponding　color　style.　Further　data　preprocessing　and　postprocessing　operations　are　designed　to　improve　the　quality　of　the　transferred　images　and　thus　prevent　negative　transfer.　DOTA　dataset　is　used　to　verify　the　performance　of　the　proposed　algorithm.　Results　show　that　after　the　style　transfer,　the　accuracy　of　object　detection　on　the　panchromatic　remote　sensing　images　has　been　significantly　improved.