基于YOLOv8-FRX的无人机小目标检测改进算法OA

To address the issues of dense small object distribution,complex backgrounds,and high false detection and missed detection rates of small objects in traditional YOLO(you only look once)algorithms for drone-captured ima-ges,an improved object detection algorithm,YOLOv8-FRX(feature refinement and eXtended detection),is pro-posed.This method enhances model performance through three main improvements:first,introducing reparameter-ised cross stage partial network(RepCSP)in the backbone network and using reparameterized convolution(Rep-Conv)on the gradient circulation branch to enhance the feature extraction capability while reducing the number of parameters;second,designing a small object enhanced pyramid(SOEP)to improve the efficiency of small object feature capture while maintaining computational efficiency;and third,adopting the Wise-IoU v3(wise intersection over union v3)strategy to optimize gradient gain allocation and enhance the accuracy of bounding box regression.Experimental results on the VisDrone dataset demonstrate that the proposed method improves the mAP50 by 4.0%compared to the baseline model while reducing parameters by 3.6%.In generalization experiments on the UAVDT(unmanned aerial vehicle detection and tracking)dataset,detection accuracy increased by 1.7%,validating the method's generality and effectiveness.These improvements not only enhance the detection capability for small ob-jects but also balance accuracy and efficiency,providing a superior solution for object detection in drone-captured images.