Arbitrarily　oriented　object　detection　in　remote　sensing　images　is　a　challenging　task.　At　present,　most　of　the　algorithms　are　dedicated　to　improving　the　detection　accuracy,　while　ignoring　the　detection　speed.　In　order　to　further　improve　the　detection　accuracy　and　provide　a　more　efficient　model　for　scenes　that　require　real-time　detection,　we　propose　an　improved　YOLOv4-CSP　network　for　rotating　object　detection　in　remote　sensing　images.　There　are　mainly　three　contributions　in　our　approach.　First,　we　design　a　new　bounding　box　regression　loss　function,　which　is　distance　and　angle-intersection　over　union.　This　loss　function　is　formed　by　adding　a　distance　penalty　term　and　an　angle　penalty　term　on　the　basis　of　intersection　over　union.　It　is　suitable　for　arbitrarily　oriented　object　detection　networks.　Second,　we　develop　an　adaptive　angle　setting　method　for　anchors　based　on　the　k-means　clustering　algorithm.　This　method　can　obtain　representative　angles　for　better　representing　the　distribution　of　the　angle　set.　By　assigning　representative　angles　to　all　anchors　for　training,　it　is　beneficial　to　reduce　the　complexity　of　the　network　to　adjust　anchors　to　ground-truth　bounding　boxes.　Finally,　we　improve　the　YOLOV4-CSP　network　and　make　it　suitable　for　detection　scenarios　based　on　rotated　anchors　by　applying　rotation　transformations.　We　combine　the　aforementioned　methods　and　use　the　final　network　to　perform　the　detection　task.　The　experimental　results　on　three　remote　sensing　datasets,　i.e.,　HRSC2016,　UCAS-AOD,　and　SSDD+,　validate　the　effectiveness　of　our　method.　Comparison　results　with　state-of-the-arts　methods　demonstrate　that　our　method　can　be　used　to　significantly　improve　the　detection　accuracy　with　a　higher　detection　speed.