In　order　to　solve　the　problems　of　false　or　missed　object　detection　of　aircrafts　in　complex　airport　scene,　which　is　caused　by　obstruction　and　poor　lighting,　this　paper　proposes　an　improved　YOLOv5　object　detection　algorithm　in　airport　scene.　Firstly,　a　Contextual　Transformer　Networks　(CoT)　is　introduced　in　the　feature　extraction　module.　Secondly,　a　detection　layer　is　added　to　the　feature　fusion　module　to　improve　the　ability　of　perception　of　detailed　features.　Finally,　decoupling　is　performed　in　the　detection　head.　Different　branches　is　used　for　classification　and　regression　tasks　to　improve　detection　performance.　The　experiments　use　the　Aircraft　Dataset　Airport　Scene　for　model　training　and　validation.　The　results　show　that　our　improved　algorithm　achieves　94.1%　and　92.3%　of　the　mAP@0.5　and　mAP@0.5:0.95,　which　are　2.8%　and　7.5%　higher　than　the　original　YOLOv5　algorithm,　respectively.　At　the　same　time,　it　ensured　the　detection　speed　well　and　met　the　real-time　requirements.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.