The object detection technology of optical remote sensing images has been widely applied in military investigation, traffic planning, and environmental monitoring, among others. In this paper, a method is proposed for solving the problem of small object detection in optical remote sensing images. In the proposed method, the hybrid domain attention units (HDAUs) of channel and spatial attention mechanisms are combined and employed to improve the feature extraction capability and suppress background noise. In addition, we designed a multiscale dynamic weighted feature fusion network (MDW-Net) to improve adaptive optimization and deep fusion of shallow and deep feature layers. The model is trained and tested on the DIOR dataset, and some ablation and comparative experiments are carried out. The experimental results show that the mAP of the proposed model surpasses that of YOLOv5 by a large margin of +2.3 and has obvious advantages regarding the detection performance for small object categories, such as airplane, ship, and vehicle, which support its application for small target detection in optical remote sensing images.