Research and implementation of dynamic multi-scale target detection model for UAV surveillance
In the fields of UAV reconnaissance,security monitoring,and autonomous driving,target detection technology faces significant challenges. Targets in images often exhibit multi-scale attributes,making detection of small-sized targets particularly difficult,and targets are prone to various degrees of occlusion. To address these pressing issues,this paper proposes an innovative dynamic multi-scale target detection model:YOLO-DDE. Firstly,novel CEMA and CED convolutional modules are introduced to enhance the backbone network's ability to handle multi-scale information and extract fine features,thus achieving more precise recognition in complex scenes. Additionally,the FPAN network structure is innovatively restructured into the DFPN structure,which employs longitudinal cross-scale fusion technology to significantly improve the model's scale feature fusion effect.Finally,a dynamic detection head is introduced,proposing the DD-Head structure,which strengthens the model's ability to handle downstream tasks. In summary,the proposed YOLO-DDE model,with its dynamic multi-scale structure,provides new possibilities for improving target detection technology performance.Experiments on the PASCAL VOC dataset were conducted to validate the proposed model. Compared to the current state-of-the-art model YOLOv8,the YOLO-DDE model achieves a 1.8％ and 3.2％ improvement in evaluation metrics map50 and map50-95,respectively. Furthermore,generalization experiments on the VisDrone,HIT-UAV,and FAIR1M2.0 datasets validate the model's strong generalization ability.
万方数据
维普
