Paper Info
Title
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images
Abstract
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large-scale variation, complex background interference, and nonuniform density distribution greatly limit the counting accuracy, particularly striking in remote-sensing imagery. To mitigate the above issues, this article proposes a novel framework for dense object counting in remote-sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve nonuniform density distribution to a certain extent. Extensive experiments on four remote-sensing counting datasets demonstrate the effectiveness of the proposed method and its superiority compared with state of the arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at https://github.com/gaoguangshuai/psgcnet.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3153946
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Remote sensing, Convolution, Feature extraction, Task analysis, Bayes methods, Marine vehicles, Solid modeling, Bayesian loss (BL), global context, object counting, pyramidal scale, remote sensing
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Guangshuai Gao100.68
Qingjie Liu29218.60
Zhenghui Hu300.34
L. Li478.13
Qi Wen500.34
Yunhong Wang63816278.50