In　open　traffic　scenarios,　gesture　recognition　for　traffic　police　faces　significant　challenges　due　to　the　small　scale　of　the　traffic　police　and　the　complex　background.　To　address　this,　this　paper　proposes　a　gesture　recognition　network　based　on　an　improved　YOLOv11.　This　method　enhances　feature　extraction　and　multi-scale　information　retention　by　integrating　RFCAConv　and　C2DA　modules　into　the　backbone　network.　In　the　Neck　part　of　the　network,　an　edge-enhanced　multi-branch　fusion　strategy　is　introduced,　incorporating　target　edge　information　and　multi-scale　information　during　the　feature　fusion　phase.　Additionally,　the　combination　of　WIoU　and　SlideLoss　loss　functions　optimizes　the　positioning　of　bounding　boxes　and　the　allocation　of　sample　weights.　Experimental　validation　was　conducted　on　multiple　datasets,　and　the　proposed　method　achieved　varying　degrees　of　improvement　in　all　metrics.　Experimental　results　demonstrate　that　this　method　can　accurately　perform　the　task　of　recognizing　traffic　police　gestures　and　exhibits　good　generalization　capabilities　for　small　targets　and　complex　backgrounds.　©　(2025),　(Science　and　Information　Organization).　All　Rights　Reserved.