In order to solve the problems of small targets and target occlusion in UAV aerial images, an improved target detection algorithm YOLO-RC based on YOLOv8s is proposed. The Receptive-Field spatial Attention (RFA) is introduced into the backbone network structure to avoid the sharing of convolutional kernel parameters, so as to improve the image feature extraction performance of the model. The C2f module is improved and deep separation convolution is introduced to reduce the computational cost of the model. A small object detection layer of hybrid attention convolution is added to improve the detection accuracy of small objects. In order to fully consider the geometric features of the predicted image, the MPDIoU loss function is used to optimize the network. Experiments on the UAV image dataset VisDrone2019 show that the mAP@0.5 of the proposed improved algorithm is 44.7%, which is 5.4 percentage points higher than that of YOLOv8s, and the number of parameters is reduced by 1.81×106 with the addition of a small target detection layer. On the DOTAv1.0 dataset, the mAP@0.5 increased by 5.6 percentage points. The improved algorithm has stronger robustness and is suitable for UAV perspective target detection tasks.