###### Abstract  
Object detection in remote sensing imagery plays a pivotal role in various applications, including aerial surveillance and urban planning. Despite its significance, the task remains challenging due to cluttered backgrounds, the arbitrary orientations of objects, and substantial scale variations across targets. To address these issues, we proposed RFE-FCOS, a novel framework that synergizes rotation-invariant feature extraction with adaptive multi-scale fusion. Specifically, we introduce a rotation-invariant learning (RIL) module, which employs adaptive rotation transformations to enhance shallow feature representations, thereby effectively mitigating interference from complex backgrounds and boosting geometric robustness. Furthermore, a rotation feature fusion (RFF) module propagates these rotation-aware features across hierarchical levels through an attention-guided fusion strategy, resulting in richer, more discriminative representations at multiple scales. Finally, we propose a novel dual-aspect RIoU loss (DARIoU) that simultaneously optimizes horizontal and angular regression tasks, facilitating stable training and the precise alignment of arbitrarily oriented bounding boxes. Evaluated on the DIOR-R and HRSC2016 benchmarks, our method demonstrates robust detection capabilities for arbitrarily oriented objects, achieving competitive performance in both accuracy and efficiency. This work provides a versatile solution for advancing object detection in real-world remote sensing scenarios. r +
Footnote †: journal: Medical  
and regression. Although these approaches achieve higher accuracy, they usually require substantial computational resources and processing time. On the other hand, one-stage detectors, including YOLO [11] and SSD [12], directly predict bounding boxes and class labels simultaneously, offering faster detection at the expense of lower accuracy in complex scenarios. Among the one-stage methods, FCOS [13] (fully convolutional one-stage object detection) introduces an innovative, anchor-free detection approach, as shown in Figure 1. This method directly predicts object locations and categories through fully convolutional layers at every pixel location, which achieves great results in many scenarios. Nonetheless, certain challenges persist, particularly in identifying rotated objects within remote sensing images. Compared to ground-level images, remote sensing images often exhibit characteristics such as the (1) arbitrary direction of the target arrangement, (2) interference from cluttered backgrounds, and (3) both inter-class similarity and intra-class diversity. To address these unique challenges, current research extensively focuses on specialized techniques such as rotated representations and rotation-invariant feature learning. Specifically, oriented bounding boxes (OBBs), which include an angular parameter \(\theta\), offer significantly improved accuracy compared to traditional horizontal bounding boxes (HBBs). To address these challenges above, we introduce an improved version of the FCOS model, named rotated feature enhancement FCOS (RFE-FCOS). Our approach integrates two specially designed modules: a rotation-invariant learning module (RIL) and a rotation-invariant feature fusion module (RFE), which enhance the model's adaptability to different object orientations by combining feature information across multiple rotation angles. The combined use of these modules allows the model to extract comprehensive rotation-invariant features, leading to a significant improvement in detection accuracy. Additionally, we propose a novel loss function called dual-aspect RIoU loss, which simultaneously leverages rotational and horizontal feature information to optimize the model performance. Extensive experiments conducted on the DIOR-R and HRSC2016 datasets validate the effectiveness of our method, demonstrating substantial enhancements in rotated object detection accuracy. The primary contributions of this paper are as follows.