Real-time detection of remote sensing images is one of the key technical problems in the field of remote sensing application.As for current mainstream target detection algorithms, there are problems of a large number of model parameters, bad real-time performance, high power consumption and high costs on the image processor (GPU).To solve the problems, a real-time detection scheme of remote sensing images based on Field Programmable Gate Array (FPGA) is proposed.Firstly, in order to reduce the quantity of parameters and improve the detection speed, MobileNetv2 is taken as the feature extraction network, and the fusion with depth separable convolution is conducted, making the model lightweight and easy to deploy.Then, CA attention module is used to improve the detection accuracy.Finally, the floating point parameters of the model are quantified into 8-bit fixed point numbers, and the network model is deployed on FPGA after quantization.The experimental results show that on the remote sensing data set VisDrone 2019, the mean Average Precision (mAP) of the scheme designed in this paper reaches 14.79%, FPS reaches 46.78 frame/s, and average power consumption is 8 W.The detection speed is 375.4% higher than that of CPU, and the power consumption is 96.8% lower than that of GPU.The scheme can meet the requirements of real-time target detection, and can be deployed in power-limited satellite, UAVs and other equipment.