Object detection on drone imagery is widely used in many fields. However， due to the complexity of the image background， the dense small objects and the dramatic scale changes， the existing object detection on drone imagery methods are not accurate enough. In order to solve this problem， we propose an accurate object detection method for drone imagery joint self attention and branch sampling. Firstly， a nested residual structure integrating self attention and convolution is designed to achieve the effective combination of global and local information， which makes the model to focus on the object area and ignore invalid features. Secondly， we design a feature fusion module based on branch sampling to mitigate the loss of object information. Finally， an improved detector for small objects is added to alleviate the problem of sharp scale changes. Furthermore， we propose a feature enhancement module to obtain more discriminative small object features. The experimental results show that the proposed algorithm performs well in various scenarios. Specifically， the mAP₅₀ and mAP of the s model on the VisDrone2019 reached 59.3% and 37.1% respectively， an increase of 5.6% and 5.4% compared with the baseline. The mAP₅₀ and mAP on the UAVDT reached 44.1% and 24.9% respectively， an increase of 5.8% and 3.2% compared with the baseline.