Open AccessMFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images
Author(s) -
Honghui Jiang,
Tingting Luo,
Hu Peng,
Guozheng Zhang
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3381539
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Rotated object detection in remote sensing images presents a highly challenging task due to the extensive fields of view and complex backgrounds. While Convolutional Neural Networks (CNNs) and Transformer networks have made progress in this area, there is still a lack of research on extracting and fusing features for small targets in complex backgrounds. To address this gap, we have extended the RTMDet framework by introducing three modules: the Focused Feature Context Aggregation Module, the Feature Context Information Enhancement Module, and the Multi-scale Feature Fusion Module. In the Focused Feature Context Aggregation Module, we replaced the Spatial Pyramid Pooling Bottleneck (SPPFBottleneck) to better extract small target features by focusing on contextual information. The Feature Context Information Enhancement Module enhances the model’s perception of multi-dimensional temporal and spatial information. Finally, we combined the original features with the fused ones to prevent the loss of specific features during the fusion process. Our proposed model, named the Multi-scale Feature Context Aggregation Network (MFCANet), was evaluated on four challenging remote sensing datasets (MAR20, SRSDD, HRSC, and DIOR-R). The experimental results demonstrate that our method outperforms baseline models, achieving improvements of 2.13%, 10.28%, 1.46%, and 1.13% in mAP for the MAR20, SRSDD, HRSC, and DIOR-R datasets, respectively.