Object recognition technology based on high-resolution remote sensing images is widely used in the fields of land and resource monitoring and intelligence collection. Accurate and fast object detection methods are the hot spots and difficulties in the current research on remote sensing images. However， the current detection methods overly pursue improving detection accuracy while ignoring detection speed. Therefore， an improved lightweight network is proposed based on YOLOX to balance detection speed and accuracy. Firstly， for the backbone of feature extraction， a Mobilenetv3tiny is proposed to improve the detection speed by reducing the parameters of the network. Secondly， the Ghost is introduced into the feature pyramid networks to reduce the complexity of the network under the premise of ensuring detection accuracy. Finally， Alpha-IoU and VariFocal_Loss are used to optimize the loss function to improve the convergence speed and positioning accuracy of the network. The ablation experiment was carried out on the NWPU VHR-10 dataset. The results show that， compared with the baseline， the improved network has a detection accuracy increase of 0.76%， a speed increase of 19.72%， a weight of 11 M （Mega）， and a parameter reduction of 65.66%. The overall effect of the improved network is better. In addition， comparative experiments on the DIOR dataset show that the detection speed is improved by 26.88% while ensuring high detection accuracy. And that proves the effectiveness of the improved network. Therefore， the improved network can effectively balance detection speed and accuracy and is easy to deploy， which makes it suitable for real-time detection of remote sensing image targets.