Eagle-Eye-Inspired Attention for Object Detection in Remote Sensing

Remote Sensing

Kang Liu¹  Xuelong Li¹  Ju Huang¹ 

[1] School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China;

关键词: remote sensing;    object detection;    eagle-eye fovea network (EFNet);    anchor-free;    attention mechanism;   

DOI  :  10.3390/rs14071743

来源: DOAJ

【 摘 要 】

Object detection possesses extremely significant applications in the field of optical remote sensing images. A great many works have achieved remarkable results in this task. However, some common problems, such as scale, illumination, and image quality, are still unresolved. Inspired by the mechanism of cascade attention eagle-eye fovea, we propose a new attention mechanism network named the eagle-eye fovea network (EFNet) which contains two foveae for remote sensing object detection. The EFNet consists of two eagle-eye fovea modules: front central fovea (FCF) and rear central fovea (RCF). The FCF is mainly used to learn the candidate object knowledge based on the channel attention and the spatial attention, while the RCF mainly aims to predict the refined objects with two subnetworks without anchors. Three remote sensing object-detection datasets, namely DIOR, HRRSD, and AIBD, are utilized in the comparative experiments. The best results of the proposed EFNet are obtained on the HRRSD with a 0.622 $AP$ score and a 0.907 ${AP}_{50}$ score. The experimental results demonstrate the effectiveness of the proposed EFNet for both multi-category datasets and single category datasets.

【 授权许可】

Unknown