Multi-scale Object Detection of Remote Sensing Images Based on Efficient Feature Extraction and Transformer

doi:10.1109/CAC59555.2023.10452032

IR > 外国语学院

	Multi-scale Object Detection of Remote Sensing Images Based on Efficient Feature Extraction and Transformer
	Li, Ce; Ma, Lin; Zhang, Jianwei; Zhao, Shutian; Wang, Zongshun; Jiang, Ruilong
	2023
会议名称	2023 China Automation Congress, CAC 2023
会议录名称	Proceedings - 2023 China Automation Congress, CAC 2023
页码	8300-8305
会议日期	November 17, 2023 - November 19, 2023
会议地点	Chongqing, China
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	Object detection in remote sensing images is a hot research topic in the field of geosciences and remote sensing. In recent years, many studies have successfully used Convolutional Neural Networks(CNN) to improve the performance of object detection in remote sensing images. CNN efficiently captures high-level semantic features through convolution and pooling operations, but occasionally loses detailed features related to small objects, which cannot solve the multi-scale problem of remote sensing images well. In addition, CNN is conducive to the extraction of local features, there are some defects in the grasp of global information. However, Transformer based on attention mechanism can obtain long-distance RSI relationship. Therefore, this paper studies the remote sensing object detection Transformer. Specifically, The EFET framework proposed in this paper is a combination of CNN and Transformer. CNN is used to capture the multi-scale features of the detected objects, and Transformer is used to encode and decode the extracted features to complete the object detection task of remote sensing images. We output the feature maps of each stage of the backbone network through Cross-Link to enhance the modeling of object information at different scales. Considering the information loss caused by the 1x1 convolution operation on the channel dimension of the feature map, the channel attention is introduced to capture the inter-channel dependence, the recognition of small targets has a good detection effect. For the over-fitting problem of remote sensing image datasets, data enhancement is combined with the model to improve the application ability of the model. A large number of experiments on NWPU VHR-10 and DIOR object detection datasets verify the effectiveness of our method. © 2023 IEEE.
关键词	Convolution Convolutional neural networks Extraction Feature extraction Image enhancement Large datasets Object recognition Remote sensing Semantics Channel attention Convolutional neural network Feature map Features extraction Image-based Multi-scales Objects detection Remote sensing images Remote-sensing Transformer
DOI	10.1109/CAC59555.2023.10452032
收录类别	EI
语种	英语
EI入藏号	20241515852494
EI主题词	Object detection
EI分类号	716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 802.3 Chemical Operations
原始文献类型	Conference article (CA)
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170543
专题	外国语学院图书馆
通讯作者	Li, Ce
作者单位	School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Li, Ce,Ma, Lin,Zhang, Jianwei,et al. Multi-scale Object Detection of Remote Sensing Images Based on Efficient Feature Extraction and Transformer[C]:Institute of Electrical and Electronics Engineers Inc.,2023:8300-8305.