The characterization ability of neural networks provides a convenient tool for remote-sensing image object detection tasks. However, the current high computational cost of mainstream neural network models has limited their application to real-time object detection tasks in remote-sensing images. This paper presents a lightweight neural network model for real-time object detection in re- mote-sensing images. The experimental results show that the method proposed in this paper keeps the detection accuracy equivalent to Yolov3, the model size is about one fifteen of Yolov3, and the network model can achieve a better balance in object detection accu- racy and computational overhead.