In this paper, a lightweight optical remote sensing image target detection algorithm LW-YOLO is proposed based on the YOLOv5 detection model to solve the difficulty of deploying the deep learning target detection algorithm on the satellite due to the large volume of the model and too many parameters. First, a lightweight Ghost module is introduced to replace the ordinary convolution in the network to reduce the number of parameters and solve the computational overhead caused by feature information redundancy in the YOLOv5 network. Then, a space and channel Fusion Attention (FA) module is designed, and the bottleneck layer FABotleneck of the network is reconstructed to further reduce the parameters and improve the positioning ability of the algorithm for optical remote sensing image targets. Finally, a sparse parameter adaptive network pruning method is proposed to prune the network and further compress the model size. Experiments on the DOTA dataset show that compared with YOLOv5s, the LW-YOLO algorithm reduces 64.7% of parameters, 62.7% of model size, 3.7% of reasoning time, and only 6.4% of mean precision. The algorithm achieves the lightweight of the network model at the cost of small accuracy loss and provides a theoretical basis for on-orbit target detection in spaceborne optical images.