Object　detection　using　unmanned　aerial　vehicle　(UAV)　remote　sensing　images　is　a　challenging　task　due　to　varying　object　scales,　dense　distribution,　and　the　predominance　of　small　object.　Directly　using　a　generalized　object　detector　that　has　not　been　specially　designed　makes　it　difficult　to　balance　accuracy　and　model　complexity.　To　address　this　challenge,　we　propose　a　lighter　and　more　accurate　network　(LMANet)　model.　First,　a　more　effective　loss　function　called　IMIoU　has　been　developed　by　combining　the　concepts　of　minimum　point　distance　bounding　box　regression-based　loss　with　auxiliary　edge-assisted　regression.　Second,　the　model　output　reconstruction　(MOR)　was　used　to　optimize　the　structure　for　small　target　objects.　Third,　we　have　designed　an　efficient　feature　extraction　module　(EFEM)　that　can　effectively　enhance　the　feature　extraction　capability　of　the　backbone　network　for　complex　environmental　information.　Finally,　to　reduce　the　computational　overhead　of　the　model,　we　have　designed　a　feature　fusion　lightweight　strategy　(FFLS)　in　the　neck　part,　which　significantly　reduces　the　computational　and　parametric　quantities　of　the　model.　The　results　of　the　LMANet　on　the　VisDrone-2021DET　and　HIT-UAV　datasets　demonstrate　a　4.7%　and　2.3%　improvement　in　mean　average　precision　(mAP),　respectively,　compared　to　the　benchmark　model.　Additionally,　the　model＇s　parameters　and　computation　are　reduced　by　79.3%　and　12.6%,　respectively.