Image Feature Point Matching Algorithm Based on Attention and Hierarchical Features

Authors

Na Chen, School of Mathematics and Statistics, Hubei University, Wuhan 430062, China
Jiajia Bai, School of Mathematics and Statistics, Hubei University, Wuhan 430062, China
Qiyin Zhou, Guangdong Provincial Institute of Artificial Intelligence and Advanced Computing , Guangzhou Key Laboratory of Visual Perception for Intelligent Driving, Guangzhou 510535, China
Jialin Li, Institute of System Optimization and Decision Science, North China Electric Power University, Beijing 100096, China

Abstract

Abstract: Feature point detection and matching is one of the core technologies in the field of intelligent driving. Aiming at the lack of consistency and continuity of feature points extracted by the existing algorithms, as well as the problem of easily ignoring the contextual semantic information when matching, this paper proposes an image feature point matching algorithm based on attention and hierarchical features (AHMF). In the feature point detection stage, differential interaction attention module (DIAM) is proposed to enhance the model's attention to the salient regions so as to improve the robustness of the feature points; further introduction of hierarchical feature enhancement module (HFEM), which extracts the hierarchical information in the feature map through the semantic feature enhancement branch and the cross-scale feature enhancement branch to improve the discriminative nature of the descriptors; in the feature point matching stage, the traditional mutual neighbor matching (MNN) algorithm is optimized, and a multi-frame image feature point matching method to screen out stable feature points. Experimental results show that compared with D2-Net, R2D2 and other algorithms, the proposed algorithm has a higher matching accuracy on the two public datasets, has a smaller number of parameters, and shows a good anti-noise interference ability.

Recommended Citation

Chen, Na; Bai, Jiajia; Zhou, Qiyin; and Li, Jialin (2025) "Image Feature Point Matching Algorithm Based on Attention and Hierarchical Features," Journal of System Simulation: Vol. 37: Iss. 11, Article 12.
DOI: 10.16182/j.issn1004731x.joss.25-0476
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/12

First Page

2839

Last Page

2852

CLC

TP391.41

Recommended Citation

Chen Na, Bai Jiajia, Zhou Qiyin, et al. Image Feature Point Matching Algorithm Based on Attention and Hierarchical Features[J]. Journal of System Simulation, 2025, 37(11): 2839-2852.

Corresponding Author

Li Jialin

DOI

10.16182/j.issn1004731x.joss.25-0476