The 4D millimeter wave radar has gained increasing attention in autonomous driving due to its robustness against environmental interference compared to other perception devices such as cameras or LiDAR. However, the practical deployment of radar remains challenging due to the noise and high sparsity of radar point clouds, as well as the resource limitations of edge computing. To address these issues, we propose Radar-Mamba, a lightweight and efficient radar enhancement approach for boosting radar perception. The proposed approach contains three main components: cross-modal alignment, radar enhancement architecture based on the Mamba model, and Doppler feature fusion. Specifically, the point clouds of radar and LiDAR are first refined and aligned to build more dense and rich occupancies. The aligned 4D radar data is then enhanced by capturing both local and global spatial-temporal features, while integrating radar-specific velocity and elevation information for further denoising. Experiments on two open-source datasets demonstrate that our method achieves state-of-the-art performance and generates high-quality 4D point clouds with a density comparable to LiDAR while maintaining a low parameter count friendly for practical deployment.

Remote sensing object detection has been an important and challenging research hot spot in computer vision that is widely used in military and civilian fields. Recently, the combined detection model of convolutional neural network (CNN) and transformer has achieved good results, but the problem of poor detection performance of small objects still needs to be solved urgently. This letter proposes a deformable end-to-end object detection with transformers (DETR)-based framework for object detection in remote sensing images. First, multiscale split attention (MSSA) is designed to extract more detailed feature information by grouping. Next, we propose multiscale deformable prescreening attention (MSDPA) mechanism in decoding layer, which achieves the purpose of prescreening, so that the encoder–decoder structure can obtain attention map more efficiently. Finally, the A–D loss function is applied to the prediction layer, increasing the attention of small objects and optimizing the intersection over union (IOU) function. We conduct extensive experiments on the DOTA v1.5 dataset and the HRRSD dataset, which show that the reconstructed detection model is more suitable for remote sensing objects, especially for small objects. The average detection accuracy in DOTA dataset has improved by 4.4% (up to 75.6%), especially the accuracy of small objects has raised by 5%.