MFO-Net: A Multiscale Feature Optimization Network for UAV Image Object Detection

Object detection in scenes captured by unmanned aerial vehicles (UAVs) is an active research area. However, the performance and efficiency of current small object detection models for UAV images are far from reaching the desired level. The inherent limitations of the features of the small objects themselves and the inconsistency of the contextual information in the feature maps lead to a degradation of the final detection performance. In this letter, to improve the performance of UAV image small object detection, we propose a multiscale feature optimization network, named MFO-Net. We have designed three crucial modules: feature optimization fusion (FOF) module, multiscale localized feature aggregation (MLFA) module, and feature enhancement (FE) module. FOF module enhances the fusion of features with inconsistent contexts at different levels by learning pixelwise displacement, facilitating more effective feature fusion, which further helps focus on and capture critical information about small objects. MLFA module aggregates richer contextual information through multibranch stripe convolution blocks, while the FE module extracts richer gradient flow information, suppresses incompatible information, and enhances feature representation capability. We conduct extensive experiments on the challenging VisDrone2019 dataset and compare the results against those obtained from the state-of-the-art methods. The experimental results show that MFO-Net performs better than other detectors. Specifically, MFO-Net achieves the best performance with 22.3% AP, 38.9%
$\text {AP}_{{50}}$
, and 22.5%
$\text {AP}_{{75}}$
on VisDrone2019. Code is available at
https://github.com/Lanziyang121/MFO-Net
.