Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Chinese Journal of Aeronautics

DOI: 10.1016/j.cja.2025.103456

Authors: Mengchu TIAN, Meiji CUI, Zhimin CHEN, Yingliang MA, Shaohua YU

Institution: Nanjing University of Science and Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,000

Funders: National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province