Object detection is a fundamental task in computer vision. Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). First, we incorporate cascaded group attention (CGA) into the attention-driven feature interaction module. By capturing features at different levels, it enhances the interaction between features through cascading and improves computational efficiency. Additionally, we propose an enhanced bidirectional feature pyramid network (EBiFPN) to facilitate multi-scale feature fusion. By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. Experimental results on the satellite imagery multi-vehicles dataset (SIMD) and the high-resolution remote sensing object detection (TGRS-HRRSD) dataset show that the improved algorithm achieved mean average precision (mAP) of 78.2% and 91.6% at an intersection over union threshold of 0.5, respectively, which is an improvement of 2.0% and 1.5% over the baseline model. This result demonstrates the effectiveness and robustness of our proposed method for remote sensing image object detection.

Images captured under weak illumination typically suffer from low brightness and contrast, severe color distortion, and significant noise contamination, which not only degrade human visual perception but also hinder the performance of high-level vision tasks. Low-light image enhancement aims to improve visual quality and provide favorable conditions for subsequent image processing. To address the challenges of non-uniform illumination and loss of details in dark regions, we propose a two-stage denoising diffusion model (two-stage DDM). Specifically, we design a convolution-based Retinex decomposition module to achieve fast and robust image decomposition, followed by a two-stage diffusion-based denoising process that further enhances global image details, brightness, and contrast. In addition, we introduce a feature enhancement module to strengthen the representational capacity of the reflectance component. To evaluate the robustness and generalization ability of the proposed model, extensive experiments are conducted on the LOLv1, LOLv2-real, and LSRW datasets. Experimental results demonstrate that the proposed two-stage DDM achieves competitive performance with state-of-the-art methods, producing enhanced images with more natural and spatially uniform brightness, along with noticeably improved visual quality.