轻量级Y0L0v10模型在无人机平台目标检测中的应用研究

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中图分类号：TP183；V279

文献标识码：A 文章编号：2096-4706（2025）23-0059-05

Abstract：Although significant progress hasbeen made incurrent lightweight Object Detection models，theystilface threecorechalenges indeploymenton UnmannedAerialVehicle（UAV）platforms.Thesechallenges includepooperformance insmall ObjectDetection，reduced detectionstabilitycausedbymeteorologicalinterference anddynamic backgrounds，anda fundamentalcontradictionbetweenmodelcomplexityhardwareresourceconstraints，andendurancerequirements.Tisstudy proposesalightweightYOLOv10optimization framework thatadresss these issues through multi-dimensional collaborative optimization.Itadopts lightweight techniques suchaschannelpruningandQuantization Aware Training todeeplyoptimize the model，nabling the model tomaintain high detectionaccuracywhilesignificantlyreducingcomputationalloadand memory usage.The frameworkintroduces theSAAMtoenhancesmallObjectDetectioncapabilitiesandemploysadynamicbackground stabilitytraining strategytoimprove themodel'sadaptabilityincomplexenvironments.ItisimplementedontheJetsonplatform andprovidesanObjectDetectionsolutionwithhighprecision，strongstabilityandlowpowerconsumptionforUAVplatfoms.

Keywords： UAV; lightweight; YOLOv10; SAAM

0 引言

近年来，无人机由于具备灵活机动、价格低廉、快速部署等优势，在城市安防、电力巡检、环境监测等领域得到了较为广泛的应用，在城市安防上能够实现实时监测人群聚集、车辆违规停放等问题；在电力巡检过程中能快速查找输电线上绝缘子破损、异物悬挂等问题[1。（剩余8591字）