[1]ZHAO Wenqing,LI Yiye.Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):399-409.[doi:10.11992/tis.202508009]
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Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 399-409 Column: 学术论文—机器学习 Public date: 2026-05-16
Title:
Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion
Author(s):
ZHAO Wenqing1; 2;  LI Yiye1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071003, China
Keywords:
remote sensing image; object detection; multi-scale; feature fusion; dynamic hypergraph; semantic features; coordinate attention; dilated convolution
CLC:
TP391
DOI:
10.11992/tis.202508009
Abstract:
Remote sensing images exhibit significant variations in target scales and complex backgrounds, while existing object detection models suffer from limited multi-scale perception and insufficient global semantic modeling capabilities. To address these challenges, a remote sensing object detection framework based on dynamic hypergraph and multi-scale feature fusion was proposed. First, a multi-scale dilated convolution feature fusion module was constructed, and a feature extraction network was designed to fully extract multi-scale features. Second, a dynamic gated hypergraph module was developed to establish a global semantic feature modeling network, which enhanced target feature perception while weakening complex background interference. Finally, a multi-channel coordinate attention module was presented, combining coordinate attention mechanisms with multi-scale channel interactions to strengthen feature representation. Ablation experiments are conducted on the DIOR and the RSOD datasets, demonstrating that the proposed model achieves 2.5 and 2.3 percent age point improvements in mean average precision over the YOLO11 baseline. Comparative experiments validate the superiority of the proposed model, showing enhanced detection performance against other methods.
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