液晶与显示2026，Vol.41Issue(2)：222-239,18.DOI:10.37188/CJLCD.2025-0245
融合隐藏状态压缩与空间依赖感知的Mamba-Transformer雾天目标检测方法
Mamba-Transformer foggy object detection method integrating hidden state compression and spatial dependency perception
摘要
Abstract
Object detection in foggy and dynamic environments remains challenging,especially for small objects whose features are easily degraded by low visibility.Existing methods often suffer from insufficient robustness and limited real-time performance.This study aims to develop a high-efficiency and highly robust detection framework tailored for foggy small-object detection.We propose HS-MambaDet,a hybrid state-space detection network built upon the Mamba-Transformer architecture.A hidden-state compression-enhanced backbone(HSM-SSD)is designed to reduce computational overhead while preserving global dependency modeling.Additionally,a Multi-scale Frequency-Spatial Fusion(MFSF)module—consisting of a high-frequency perception branch and a spatial dependency modeling branch—is introduced to enhance fine-grained details and long-range spatial relations for small objects.The overall framework integrates CNN,Transformer,and Mamba components to jointly capture local textures,global context,and efficient sequential dependencies.Extensive experiments on the RTTS and CityScapes datasets demonstrate that HS-MambaDet outperforms mainstream SOTA models in both accuracy and efficiency.On RTTS,the complete model achieves 87.3%precision,73.1%recall,81.2%mAP@0.5,and 51.0%mAP@(0.5～0.95),exceeding the best comparison baselines by up to 3.8%,3.9%,3.6%,and 3.3%,respectively.The inference time remains low at 0.26 s,ensuring real-time capability.In small-object scenarios,HS-MambaDet improves mAP@0.5 by up to 4.4%,and cross-domain tests under varying fog intensities further verify its superior generalization and robustness.By combining hidden state compression with multi-scale frequency-spatial fusion,HS-MambaDet effectively enhances fine-detail perception and spatial dependency modeling under foggy conditions.The proposed framework achieves strong performance in accuracy,robustness,and inference speed,offering a practical and efficient solution for real-time object detection in adverse weather and dynamic environments.关键词
目标检测/Mamba/高频感知/融合隐藏状态压缩Key words
object detection/Mamba/high-frequency perception/fusion hidden state compression分类
信息技术与安全科学引用本文复制引用
陈悦,许锋,宋京昊..融合隐藏状态压缩与空间依赖感知的Mamba-Transformer雾天目标检测方法[J].液晶与显示,2026,41(2):222-239,18.基金项目
十三五国家重点研发技术项目(No.2017YFC0821004) （No.2017YFC0821004）
2022辽宁省教育厅基本科研重大攻关项目(No.LJKZZ20220007) （No.LJKZZ20220007）
中央高校基本科研业务重大培育项目(No.3242022004)Supported by National Key R&D Program of China(13th Five-Year Plan)(No.2017YFC0821004) （No.3242022004）
2022 Major Fundamental Research Project of the Liaoning Provincial Department of Education(No.LJKZZ20220007) （No.LJKZZ20220007）
Major Cultivation Project for Fundamental Research in Central Universities(No.3242022004) （No.3242022004）
