上下文感知多感受野融合网络的定向遥感目标检测  

A Context-Aware Multiple Receptive Field Fusion Network for Oriented Object Detection in Remote Sensing Images

作　　者：姚婷婷[1] 肇恒鑫 冯子豪 胡青[1] YAO Tingting;ZHAO Hengxin;FENG Zihao;HU Qing(Information Science and Technology College,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学信息科学技术学院,大连116026

出　　处：《电子与信息学报》2025年第1期233-243,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62001078);中央高校基本科研业务费(3132023249)。

摘　　要：以广距鸟瞰视角拍摄获取的遥感图像通常具有目标种类多、尺度变化大以及背景信息丰富等特点,为目标检测任务带来巨大挑战。针对遥感图像成像特点,该文设计一种上下文感知多感受野融合网络,通过充分挖掘深度网络中遥感图像在不同尺寸特征描述下所包含的上下文关联信息,提高图像特征描述力,进而提升遥感目标检测精度。首先,在特征金字塔前4层网络中构建了感受野扩张模块,通过扩大网络在不同尺度特征图上的感受野范围,增强网络对不同尺度遥感目标的感知能力;进一步,构建了高层特征聚合模块,通过将特征金字塔网络中高层语义信息聚合到低层特征中,从而将特征图中所包含的多尺度上下文信息进行有效融合;最后,在双阶段定向目标检测框架下设计了特征细化区域建议网络。通过对一阶段提案进行精细化处理,提升提案准确性,进而提高二阶段兴趣区域对齐网络得到的不同成像方向下的遥感目标检测性能。在公测数据集DIOR-R和HRSC2016上的定性和定量的对比实验结果证明,所提方法对不同种类和尺度大小的遥感目标均能实现更加准确的检测。Objective Recent advances in remote sensing imaging technology have made oriented object detection in remote sensing images a prominent research area in computer vision.Unlike traditional object detection tasks,remote sensing images,captured from a wide-range bird’s-eye view,often contain a variety of objects with diverse scales and complex backgrounds,posing significant challenges for oriented object detection.Although current approaches have made substantial progress,existing networks do not fully exploit the contextual information across multi-scale features,resulting in classification and localization errors during detection.To address this,a context-aware multiple receptive field fusion network is proposed,which leverages the contextual correlation in multi-scale features.By enhancing the feature representation capabilities of deep networks,the accuracy of oriented object detection in remote sensing images can be improved.Methods For input remote sensing images,ResNet-50 and a feature pyramid network are first employed to extract features at different scales.The features from the first four layers are then enhanced using a receptive field expansion module.The resulting features are processed through a high-level feature aggregation module to effectively fuse multi-scale contextual information.After obtaining enhanced features at different scales,a feature refinement region proposal network is designed to revise object detection proposals using refined feature representations,resulting in more accurate candidate proposals.These multi-scale features and candidate proposals are then input into the Oriented R-CNN detection head to obtain the final object detection results.The receptive field expansion module consists of two submodules:a large selective kernel convolution attention submodule and a shift window self-attention enhancement submodule,which operate in parallel.The large selective kernel convolution submodule introduces multiple convolution operations with different kernel sizes to capture contextual

关 键 词：遥感图像 深度学习 目标检测 多感受野融合 

分 类 号：TN911.73[电子电信—通信与信息系统] TP751.1[电子电信—信息与通信工程]