MCD-YOLOv10n: A Small Object Detection Algorithm for UAVs

Abstract

Deep neural networks deployed on UAVs have made significant progress in data acquisition in recent years. However, traditional algorithms and deep learning models still face challenges in small and unevenly distributed object detection tasks. To address this problem, we propose the MCD-YOLOv10n model by introducing the MEMAttention module, which combines EMAttention with multiscale convolution, uses Softmax and AdaptiveAvgPool2d to adaptively compute feature weights, dynamically adjusts the region of interest, and captures cross-scale features. In addition, the C2f_MEMAttention and C2f_DSConv modules are formed by the fusion of C2f with MEMAttention and DSConv, which enhances the model's ability of extracting and adapting to irregular target features. Experiments on three datasets, VisDrone-DET2019, Exdark and DOTA-v1.5, show that the evaluation metric mAP50 achieves the best detection accuracy of 32.9%, 52.9% and 68.2% when the number of holdout parameters is at the minimum value of 2.24M. Moreover, the mAP50-95 metrics (19.5% for VisDrone-DET2019 and 45.0% for DOTA-v1.5) are 1.1 and 1.2 percentage points ahead of the second place, respectively. In terms of Recall, the VisDrone-DET2019 and DOTA-v1.5 datasets improved by 1.0% and 0.7% over the baseline model. These results validate that MCD-YOLOv10n has strong adaptability and generalization ability for small object detection in complex scenes.