Abstract: Abstract: In response to the challenges in unmanned aerial vehicle (UAV) aerial imagery, such as blurred object edge infor-mation, complex background interference, and the low resolution and difficulty in identification of small objects, this paper pro-poses a novel improved RT-DETR-based object detection method for UAV aerial images, named MCS-DETR. Initially, a multi-scale edge information enhancement module is designed within the backbone network. By constructing an edge information enhancement mechanism and integrating it with deep convolutional operations and feature fusion strategies, the module aims to extract feature information at different scales and enhance the model’s perception of image edge information. Subsequently, a convolutional additive token mixer is incorporated into the intra-scale feature interaction mechanism to optimize the feature in-teraction process and improve the model’s capability of capturing global contextual key information. Finally, based on the origi-nal feature fusion method, a small object enhancement pyramid network is proposed to enhance the model’s ability to extract detailed features of small targets. Experimental results indicate that, compared to the RT-DETR model, the MCS-DETR algo-rithm reduces the parameter size by 20.7% on the Visdrone2019-DET-Test dataset. Additionally, it achieves increases in accura-cy, recall, mAP@50, and mAP@50:95 of 2.4%, 3.1%, 2.8%, and 2.0%, respectively, effectively improving the issues of missed and erroneous detections in complex scenes for UAV aerial image object detection.

Key words: Keywords: object detection, UAV aerial imagery, RT-DETR, multi-scale edge information enhancement, convolutional additive token mixer, small object enhancement pyramid