Ground　objects　in　high-resolution　remote　sensing　images　are　often　closely　related　to　the　scene　categories.　If　the　constraint　information　of　the　scene　on　the　ground　object　can　be　usefully　employed,　it　is　expected　to　improve　further　the　performance　of　object　detection.　Considering　the　relationship　between　scene　information　and　objects,　a　scene　constrained　object　detection　method　in　high-resolution　remote　sensing　images　by　Relation-aware　Global　Attention　(RGA)　is　proposed.　First,　the　global　scene　features　are　learned　by　adding　the　global　relational　attention　to　the　basic　network　in　Feature　fusion　and　Scaling-based　Single　Shot　Detector　(FS-SSD).　Then,　object　is　predicted　by　combining　the　oriented　response　convolution　module　with　the　multiscale　feature　module　under　the　constraints　of　learned　global　scene　features.　Finally,　two　loss　functions　are　used　to　optimize　jointly　the　network　to　achieve　object　detection.　Four　experiments　are　conducted　on　NWPU　VHR-10　dataset　and　better　object　detection　performance　is　achieved　under　the　constraints　of　scene　information.　©　2022　Science　Press.　All　rights　reserved.