# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
###### Abstract  
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-HRSSD) 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. c 2020 20  
Over the past decade, with the rapid development of deep convolutional neural networks (CNNs) [9], many mainstream object detection methods are based on CNN architectures and typically categorized into one-stage [10; 11; 12; 13; 14; 15; 16] and two-stage [17; 18; 19] detectors. Region-based object detection algorithms divide the task into two stages: the first stage generates a series of candidate regions likely to contain the target, while the second stage classifies these candidate regions as target or background and performs bounding box regression. In contrast, regression-based algorithms skip candidate region generation, directly regressing target bounding boxes and categories from multiple locations in the input image, offering faster processing speeds than two-stage methods. Compared to traditional algorithms, CNNs generate strong, high-level feature representations from images. However, due to the inherent limitations of convolutional operations, particularly their locality, both single-stage and two-stage CNN-based algorithms face performance bottlenecks. In recent years, attention mechanisms have gained prominence for overcoming the short-range modeling limitations of CNNs. By assigning different weights to channels or spatial regions, attention mechanisms enable high-resolution processing of specific areas, focusing on important parts of the image rather than processing the entire image. This targeted weighting extracts key information from images while ignoring irrelevant details. Transformer-based object detection models employ self-attention mechanisms to effectively integrate global contextual information. In 2020, [PERSON] et al. [20] introduced detection transformer (DETR), the first successful application of transformer architecture in object detection. DETR treats object detection as a direct set prediction problem, using a global loss function and bipartite graph matching for unique predictions. Subsequently, researchers recognized that models employing attention mechanisms could capture global features, leading to improvements in DETR to make it applicable to various specific tasks. These enhancements have enabled DETR to achieve detection accuracy surpassing that of traditional CNN-based models.