Rotated object detection in aerial images presents unique challenges due to the variability in object orientation and aspect ratios. In object detection, the common practice is to use boxes to enclose the spatial locations of discovered objects. Traditionally, these bounding boxes are rectangular in shape with edges parallel to the horizontal or vertical axes. However, with the ever-increasing content complexity of images, it would be desirable to rotate these bounding boxes properly to better fit the sizes and shapes of all semantic object locations in images with random orientations. Lately, through different deep learning model developments, there are noticeable improvements on the detection of rotated objects in images. Certainly, it always faces challenges with the most stringent expectations and accuracy requirements. Hence, the primary objective of this study is to analyze and compare state-of-the-art methodologies for rotated object detection in aerial imagery, and provide a comprehensive survey of the latest research status of it. From four different working principles, we systematically review recent advancements in deep learning architectures, including R-CNN variants, YOLO variants and Transformer variants adapted for rotated bounding boxes. Then we further carry out quantitative performance comparisons among the latest state-of-the-art rotated object detection algorithms using the benchmark dataset DOTA and computing facility. Among them, the RTMDet ^a outperforms all other models ^b . There are numerous important applications based on aerial images, for example, urban management, precision agriculture, environment monitoring, emergency rescue, and disaster relief, etc. This study contributes to advancing the understanding of rotated object detection in aerial imagery by providing insights into effective methodologies and highlighting areas for future research. The findings guide practitioners and researchers in selecting appropriate techniques for applications requiring robust rotated object detection in challenging aerial environments.

Synthetic aperture radar (SAR) object detection remains challenging, as target evidence is often sparse, discontinuous, and heavily influenced by speckle noise, sidelobes, shadows, and clutter-like background scattering. Existing dense detectors usually process SAR images as ordinary grayscale images, which causes feature modulation to rely on unrestricted saliency responses that may simultaneously enhance true targets and bright background scatterers. To address this issue, we propose a SAR-inspired Scattering-Center Field (SCF) modulation framework for multi-scale dense object detection. The SCF module is designed as a tensor-preserving feature adapter that decomposes feature modulation into three internal latent fields: a center-like evidence field, a response-amplitude field, and an orientation-anisotropy field. These fields are inferred from intermediate features through lightweight local, strip-convolution, and contextual branches, and then they are subsequently fused into an identity-initialized residual spatial gate. The proposed module requires no scattering-center annotations, segmentation masks, auxiliary field supervision, phase history, polarimetric data, or additional post-processing. Consequently, it can be seamlessly integrated into standard dense detection pipelines without altering labels or prediction heads. Experiments are conducted on four publicly available SAR detection datasets, namely SSDD, SAR-AIRcraft-1.0, SAR-Ship, and MSAR-1.0. The proposed detector achieves mAP50/mAP50-95 scores of 0.954/0.682 on SSDD, 0.930/0.661 on SAR-AIRcraft-1.0, 0.971/0.676 on SAR-Ship, and 0.688/0.482 on MSAR-1.0. These results indicate that SCF provides a lightweight and detector-compatible modulation mechanism for improving SAR object detection under sparse target responses and cluttered imaging conditions.