@Article{cmc.2025.069340,
AUTHOR = {Haixia Wang, Huan Zhang, Xiuling Wang, Xule Xin, Zhiguo Zhang},
TITLE = {HUANNet: A High-Resolution Unified Attention Network for Accurate Counting},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {1},
PAGES = {1--20},
URL = {http://www.techscience.com/cmc/v86n1/64461},
ISSN = {1546-2226},
ABSTRACT = {Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and convolutional kernel information through an attention mechanism applied across multiple levels of the network; and the Grid-Assisted Point Matching Module (GPMM), which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms. Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD, demonstrating the effectiveness and versatility of HUANNet.},
DOI = {10.32604/cmc.2025.069340}
}