A multiscale target detection network,VTO-YOLOv8,for unmanned aerial vehicle (UAV) images is proposed to address the low accuracy of existing algorithms caused by complex backgrounds,a high proportion of smalltargets,and uneven distributions.First,wise intersection over union (WIoU) v3 was used as the bounding-box regression loss,and a wise gradient allocation strategy was employed for the network to focus more on regular quality samples and improve localization ability.Second,a four-layer target bi-directional feature pyramid network (T-BiFPN) structurewas designed to strengthen the integration ofshallow and deep features.Furthermore,a faster implementation of CSP bottleneck with diverse branch blocks (C2f-DBB) module was designed to improve the detection performance of the network withoutincreasing computationalcomplexity.In addition,a focalmodulation module wasused to enhance the interaction ofinformation atdifferentscales.The experimentalresults demonstrated thatthe proposed network improved the mean average precision (mAP) and mAP50 by 5.9% and 9.0%,respectively,compared with those of the baseline network on the Visdrone2019 dataset.Moreover,the network parameters were reduced by 22.6%.The proposed method can be applied to targetdetection in UAV aerialphotography.