计算机工程与应用2023，Vol.59Issue(24)：155-164,10.DOI:10.3778/j.issn.1002-8331.2207-0077
自适应特征细化的遥感图像有向目标检测
Adaptive Feature Refinement for Oriented Object Detection in Remote Sensing Images
摘要
Abstract
Object detection is an important and challenging task in remote sensing research.Remote sensing images are mostly taken from a top-down perspective.Due to their complex background and arbitrary orientation,object detection algorithms in natural scenes face some challenges when directly applied to remote sensing.Aiming at the above problems,this paper proposes an adaptive feature refinement network AFR-Net to generate directed candidate boxes with high matching degree to objects.Firstly,the feature enhancement module is designed to increase the feature representation with discriminative power,so as to improve the ability of capturing spatial details in complex background.Secondly,in order to obtain the directed candidate box adapted to the object direction,an adaptive feature alignment module is proposed to alleviate the spatial misalignment problem between convolution feature and directed objects,and the rotation-invariant feature is obtained.Finally,the rotation sensitive features are obtained by decoupling detection head module and accurate bounding box regression is refined.The proposed network achieves 66.71%and 97.12%accuracy in the publicly avail-able remote sensing object detection datasets DIOR-R and HRSC2016,which are 2.3 and 0.9 percentage points higher than the original algorithm,respectively.At the same time,compared with some mainstream object detection algorithms,the proposed network has certain advantages.关键词
卷积神经网络/遥感图像/目标检测/特征细化/特征对齐Key words
convolutional neural network/remote sensing image/object detection/feature refinement/feature alignment分类
信息技术与安全科学引用本文复制引用
刘恩海,许佳音,李妍,樊世燕..自适应特征细化的遥感图像有向目标检测[J].计算机工程与应用,2023,59(24):155-164,10.基金项目
河北省自然科学基金(F2020202008) （F2020202008）
河北省高等学校科学研究项目(ZD2021311). （ZD2021311）
