Paper Info
Title
Adaptive Feature Aggregation Network For Object Detection In Remote Sensing Images
Abstract
Object detection in remote sensing images is a challenging task because of the large scale variations across the geospatial objects. The feature pyramid network (FPN) is widely used to alleviate the scale variations problem, however, it only fuses the features from adjacent levels and lacks the information of the entire feature hierarchy. In this paper, we propose a novel and effective feature pyramid aggregation network, called Adaptive Feature Aggregation Network (AFANet). Specifically, we propose the Adaptive Feature Aggregation (AFA) module to adaptively aggregate multi-level features of FPN and introduce the Bottom-up Path to enhance the location information of the entire feature levels. In addition, we use the Receptive Field Block (RFB) module to capture different receptive field features for each level feature map. We evaluate the effectiveness of our AFANet on the DOTA dataset and achieves noticeable performance compared with the baseline.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9323567
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
Object detection, remote sensing images, adaptive feature aggregation, convolutional neural network
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Wenliang Sun100.34
Xiangrong Zhang200.34
Tianyang Zhang3638.35
Peng Zhu412.03
Li Gao500.34
Xu Tang600.34
Bo Liu700.34