Optimized and Improved YOLOv8 Target Detection Algorithm from UAV Perspective

Aiming at the problems of multi-scale, small target, complex background and target occlusion in unmanned aerial vehicle(UAV) view, an improved YOLOv8 algorithm BDAD-YOLO based on dynamic sample attention scale sequences is proposed. Firstly, by introducing the idea of BiFormer, the backbone structure of the original model is reformed to improve the model's attention to key information and better retain the fine-grained details of the target. Because of the variability of the size and position of the target, the traditional convolution can't handle this situation well. Therefore, based on the idea of deformable convolutional network (DCN), a C2_DCf module is designed, which can enhance the feature extraction of small targets, so as to further improve the fusion of feature information between small and medium-sized target layers in the neck network. Secondly, an attentional scale sequence fusion framework based on dynamic samples is proposed, which uses lightweight dynamic point sampling and fuses feature maps of different scales to both enhance the ability of the network and extract multi-scale information. Finally, WIoU loss function is used to improve the adverse effects of small target and low-quality data on the gradient, thereby accelerating the convergence speed of the network. The experimental results show that the average detection accuracy is increased by 4.6 percentage points and 3.7 percentage points on val set and test set in VisDrone data set respectively, and by 2.4 percentage points on DOTA data set, demonstrating the effectiveness and universality of the improved algorithm. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.