Target Detection Algorithm for UAV Aerial Images Based on IRE-YOLO

To address challenges in UAV aerial images such as a high proportion of small objects, complex backgrounds, significant scale variations, and limited compu-ting resources, this study proposes an improved object detection algorithm based on YOLOv11n—namely, IRE-YOLO. In the backbone network, the algo-rithm introduces the inverted residual mobile block (iRMB) attention mecha-nism to enhance the model's ability to extract features of small objects in com-plex backgrounds. It replaces some standard convolutions with depthwise sepa-rable convolutions (DSConv) to reduce the number of parameters while main-taining feature expression capability. In the neck network, an efficient upsam-pling block (EUCB) is designed to recover detailed information through hierar-chical feature reconstruction, thereby improving the representation quality of small objects' edges and textures. Experimental results on the DOTA dataset show that IRE-YOLO outperforms mainstream lightweight models in key metrics including precision (P), recall (R), mAP@50, and mAP@50:95, achieving 69.4%, 59.0%, 61.0%, and 34.2% respectively. Meanwhile, the model's parameter count is reduced to 8.7MB, realizing an effective balance between accuracy and light-weight design. The research demonstrates that IRE-YOLO has high practical val-ue in small object detection tasks for UAV aerial images and provides a feasible solution for real-time deployment on embedded platforms.