Aiming at the problems of false detection and missed detection of objects in UAV aerial images caused by large changes in the object scale or mutual interference between the object and background,a object detection method based on reparameterized attention is proposed,which is applied to UAV aerial object detection.Firstly,the reparameterized coordinate attention module is proposed to enhance the relevant features and improve network ability to capture context information.Secondly,a multi-scale receptive field enhancement module is designed to reconstruct the backbone network,thereby enhancing the acceptance domain of feature map and improving the feature extraction ability of network.Then,a four-scale feature fusion detection network is proposed to improve detection ability of the network for small objects.Finally,a decoupling detection head is introduced to resolve the conflict between classification and regression tasks.In experiments on the VisDrone2021 dataset,mAP0.5 and racall rate of our algorithm is improved respectively by 7.6 and 5.5 percentage points in comparison with the original algorithm,and the algorithm also shows obvious advantages over other methods.The experimental result shows that,the improved method can better solve the above-mentioned problems of false detection and missed detection,and has good detection effect.