文献类型：期刊文献

中文题名：基于移位窗口金字塔Transformer的遥感图像目标检测

英文题名：SPT:Swin Pyramid Transformer for Object Detection of Remote Sensing

作者：蔡肖[1];陈志华[1];盛斌[2]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]上海交通大学电子信息与电气工程学院,上海200240

年份：2023

卷号：50

期号：1

起止页码：105

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金(61672228);装备预研教育部联合基金(6141A02022373)。

语种：中文

中文关键词：深度学习;目标检测;遥感图像;注意力机制;Transformer

外文关键词：Deep learning;Object detection;Remote sensing;Attention mechanism;Transformer

摘要：目标检测任务是计算机视觉领域中基础且备受关注的工作,遥感图像目标检测任务因在交通、军事、农业等方面具有重要应用价值,也成为研究的一大热点。相比自然图像,遥感图像由于受到复杂背景的干扰,以及天气、小型和不规则物体等诸多因素的影响,遥感图像目标检测任务要实现较高的精度是极具挑战性的。文中提出了一种新颖的基于移位窗口Transformer的目标检测网络。模型应用了移位窗口式Transformer模块作为特征提取的骨干,其中,Transformer的自注意力机制对于检测混乱背景下的目标十分有效,移位窗口式的模式则有效避免了大量的平方级复杂度计算。在获得骨干网络提取的特征图之后,模型使用了金字塔架构以融合不同尺度、不同语义的局部和全局特征,有效地减少了特征层之间的信息丢失,并捕捉到固有的多尺度层级关系。此外,文中还提出了自混合视觉转换器模块和跨层视觉转换器模块。自混合视觉转换器模块重新渲染了深层特征图以增强目标特征识别和表达,跨层视觉转换器模块则依据特征上下文交互等级重新排列各特征层像素的信息表达。模块融入到自下而上和自上而下双向特征路径之中,以充分利用包含不同语义的全局和局部信息。所提网络模型在UCAS-AOD数据集和RSOD数据集上进行训练并测试,实验结果表明,模型在遥感图像目标检测任务上效果显著,尤其适用于不规则的目标和小目标类别,如立交桥和汽车。
The task of object detection is a basic and highly concerned work in the field of computer vision.Because object detection in remote sensing has important application value in transportation,military,agriculture,etc.,it has also become a major research hotspot.Compared with natural images,remote sensing images are affected by many factors such as complex background interference,weather,irregularities,and small objects.It is extremely challenging to achieve higher accuracy in remote sensing image object detection tasks.This paper proposes a novel object detection network based on Transformer,swin pyramid Transformer(SPT).SPT uses a sliding window Transformer module as the backbone of feature extraction.Among it,the self-attention mechanism of Transformer is very effective for detecting objects in a chaotic background,and the sliding window mode efficiently avoids a large number of square-level complexity calculations.After obtaining the feature map extracted by the backbone network,SPT uses a pyramid architecture to fuse different scale and semantic features,pithily reducing the loss of information between feature layers,and capturing the inherent multi-scale hierarchical relationship.In addition,this paper proposes self-mixed Transformer(SMT)module and cross-layer Transformer(CLT)module.SMT re-renders the highest-level feature map to enhance object feature recognition and expression.According to the feature context interaction,the feature expressions of the pixels of each feature layer are rearranged by CLT,and the CLT module is integrated into the bottom-up and top-down dual paths of the pyramid to make full use of global and local information containing different semantics.Our SPT network model is trained and tested on the UCAS-AOD and RSOD datasets.Experimental results show that SPT is high-performing in remote sensing image object detection tasks,especially suitable for irregular and small target categories,such as overpass and car.