In view of the fact that the detection effect of EfficientNet-YOLOv3 object detection algorithm is not very good, this paper proposes a small object detection research based on dynamic convolution neural network. Firstly, the dynamic convolutional neural network is introduced to replace the traditional, which makes the algorithm model more robust; secondly, the optimization parameters are continuously adjusted in the training process to further strengthen the model structure; finally, the Learning Rate and Batch Size parameters are modified during the training process in order to prevent overfitting. In order to verify the effectiveness of the proposed algorithm, RSOD and TGRS-HRRSD remote sensing image data sets are used to test the effect. The results of the proposed algorithm on RSOD remote sensing image data sets show that compared with the original EfficientNet-YOLOv3 algorithm, the mean Average Precision (mAP) value is increased by 1.93% and the mean Log Average Miss Rate (mLAMR) value is reduced by 0.0500; The results of the proposed algorithm on TGRS-HRRSD remote sensing image data set show that compared with the original EfficientNet-YOLOv3 algorithm, the mAP value is increased by 0.07% and the mLAMR value is reduced by 0.0007.