基于DBB-YOLOv10s的无人机小目标检测OA

To address the challenges of complex background interference and multiscale target detection in UAV-based small object detection tasks,this study proposes a network model based on DBB-YOLOv10s.Using YOLOv10s as the baseline model,the proposed network incorporates dilated convolution(DC2f)to expand the receptive field,a bidirectional feature pyramid network(BiFPN)to achieve multiscale feature fusion,and a BAM attention mechanism to enhance the model's focus on target regions.Experimental results on the VisDrone2021 dataset demonstrate that the proposed algorithm achieves strong performance,with a mean average precision(mAP)of 41.8%and an inference speed of 148 FPS,while maintaining a model size of 6.59 M parameters,ensuring strong practicality.Compared with existing YOLO models and their variants,the proposed model not only maintains high detection accuracy in complex scenarios but also balances real-time performance and computational efficiency,making it suitable for deployment in embedded systems and real-time UAV monitoring tasks.The results indicate that the proposed algorithm significantly enhances detection capability and generalization performance in complex environments.