基于自适应特征融合的无人机小目标检测算法OA北大核心CSTPCD

This paper proposes a target detection algorithm for images captured by drones,aiming to solve the issues of serious missed detection and low detection accuracy for small objects from a drone's perspective.The main improvements include redesigning the clustering algorithm to generate more accurate prior boxes,introducing an adaptive feature fusion module to enable the model to more flexibly learn contextual feature information,and modifying the detection head to perform object detection on larger feature maps while decoupling classification and regression tasks.Through extensive experiments on the VisDrone2019 dataset,the improved YOLOv5s model showed a 5.8%increase in mAP50 over the baseline model and maintained a high frame rate(67 FPS).The experimental results demonstrate that the proposed improvements significantly enhance the model's detection performance,making it suitable for complex scenarios captured by drones.