A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting

doi:10.1109/TGRS.2023.3238185

CQUET-IR > 智慧健康学院

	A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting
	Yi, Jun 1; Shen, Zhilong 1; Chen, Fan 1; Zhao, Yiheng 1; Xiao, Shan2 ; Zhou, Wei 1
	2023
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
卷号	61
摘要	In recent decades, remote sensing object counting has attracted increasing attention from academia and industry due to its potential benefits in urban traffic, public safety, and road planning. However, this issue is becoming a challenge for computer vision because of various technical barriers, such as large-scale variation, complex background interference, and nonuniform density distribution. Recent results show hopeful prospects for object counting using convolutional neural networks (CNNs), but most existing CNN-based methods draw on larger and more complex architectures, which leads to a huge computational and storage burdens, severely limiting their application in real-world scenarios. In this article, a lightweight multiscale feature fusion network for remote sensing object counting, named LMSFFNet, is presented to achieve a better balance between the running speed of the network and the counting accuracy. Specifically, in the encoding process, we select a MobileViT module as the backbone of the network to reduce the numbers of network parameters and computing cost. In return, a cascade structure of the channel-spatial attention mechanisms compensates for the weaker feature extraction ability of the lightweight network. In the decoding process, a lightweight multiscale context fusion module (LMCFM) as a multiscale feature fusion module is developed to solve the problem that the number of parameters increases with the expansion of the object scale when extracting multiscale features. In addition, a lightweight counting scale pooling module (LCSPM) is used to mine the subtle features of the target object. Two kinds of typical object counting experiments, namely, experiments on remote sensing benchmarks (RSOC dataset) and crowd benchmarks (ShanghaiTech, UCF-QNRF, and UCF_CC_50 datasets), show the effectiveness of the proposed method. © 1980-2012 IEEE.
关键词	Accident prevention Complex networks Decoding Extraction Job analysis Network architecture Neural networks Remote sensing Attention mechanisms Benchmark testing Decoding Efficient and lightweight network Features extraction Features fusions Interference Multi-scale features Multiscale feature fusion Object counting Remote-sensing Task analysis
DOI	10.1109/TGRS.2023.3238185
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000925114100004
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20230613543370
原始文献类型	Journal article (JA)
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/17737
专题	智慧健康学院
作者单位	1.Chongqing University of Science and Technology, College of Intelligent Technology and Engineering, Chongqing; 401331, China; 2.Chongqing College of Electronic Engineering, Institute of Big Data and Optimization, Chongqing; 401331, China
推荐引用方式 GB/T 7714	Yi, Jun,Shen, Zhilong,Chen, Fan,et al. A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61.
APA	Yi, Jun,Shen, Zhilong,Chen, Fan,Zhao, Yiheng,Xiao, Shan,&Zhou, Wei.(2023).A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61.
MLA	Yi, Jun,et al."A Lightweight Multiscale Feature Fusion Network for Remote Sensing Object Counting".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023).