Dealing　with　the　insufficient　detection　accuracy　and　speed　of　aircraft　targets　in　remote　sensing　images　under　complex　background,　this　paper　proposes　a　new　detection　method,　YOLOv5-Aircraft,　based　on　the　YOLOv5　network.　The　YOLOv5-Aircraft　model　is　improved　in　3　ways:　(1)　At　the　beginning　and　end　of　original　batch　normalization　module,　centering　and　scaling　calibration　are　added　to　enhance　the　effective　features　and　form　a　more　stable　feature　distribution,　which　strengthens　the　feature　extraction　ability　of　network　model.　(2)　The　cross-entropy　loss　function　in　the　confidence　of　the　original　loss　function　is　improved　to　the　loss　function　based　on　smoothed　Kullback-Leibler　divergence.　(3)　For　reducing　information　loss,　the　CSandGlass　module　is　designed　on　the　backbone　feature　extraction　network　of　YOLOv5　to　replace　the　residual　module.　Meanwhile,　low-resolution　feature　layers　are　eliminated　to　reduce　semantic　loss.　Experiment　results　demonstrate　that　the　YOLOv5-Aircraft　model　can　enhance　the　accuracy　and　speed　of　aircraft　target　detection　in　remote　sensing　images　while　achieving　easier　convergence.　©　2021　IEEE.