MFR-YOLOv10: Object detection in UAV-taken images based on multilayer feature reconstruction network

When detecting objects in Unmanned Aerial Vehicle (UAV) taken images, large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once (YOLO) framework, rendering them challenging to deal with tasks that demand high precision. To address these problems, this paper proposes a high-precision object detection algorithm based on YOLOv10s. Firstly, a Multi-branch Enhancement Coordinate Attention (MECA) module is proposed to enhance feature extraction capability. Secondly, a Multilayer Feature Reconstruction (MFR) mechanism is designed to fully exploit multilayer features, which can enrich object information as well as remove redundant information. Finally, an MFR Path Aggregation Network (MFR-Neck) is constructed, which integrates multi-scale features to improve the network’s ability to perceive objects of var-ying sizes. The experimental results demonstrate that the proposed algorithm increases the average detection accuracy by 14.15% on the VisDrone dataset compared to YOLOv10s, effectively enhancing object detection precision in UAV-taken images.