Abstract:To address the problems of diverse target scales and the difficulty in identifying small-scale targets in UAV aerial images, this study proposes a multi-scale target recognition algorithm named MON-YOLOv8n. First, the C2f_OCA module is designed based on the OrthoNets orthogonal channel attention network. By integrating attention mechanisms with orthogonal channel design, this module enhances the performance of convolutional neural networks for multi-scale feature extraction and improves the model’s multi-dimensional understanding of complex data. Second, the multi-scale edge enhancement module (MEEM) and multi-order gated aggregation module (MOGA) are introduced to enhance the edge details of objects and effectively reduce redundant information in the features. Then, the SPDConv and RepViTBlock modules are introduced into the backbone and neck networks, respectively, to achieve a quantitative reduction in model parameters and improve the detection capability of small-scale targets. Finally, the target detection layer is modified to utilize the NWDLoss function, further enhancing the model’s ability to detect small targets and improving its robustness. Experimental results show that the proposed model achieves detection mAP50 of 95.2%, 85.2%, and 84.1% on HIT-UAV, DroneVehicle, and DOTAv1 datasets, respectively, which are 7.2%, 4.8%, and 5.0% higher than the YOLOv8n baseline model.