SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection

TianYi Yu; DaJian Zhong; Lilin Wang

cs.CVMay 2, 2025

SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection

Authors: TianYi Yu, DaJian Zhong, Lilin Wang

Abstract

Traffic sign detection is a challenging visual signal processing task for advanced driver assistance, where small objects, scale variation, and occlusion limit conventional detectors with fixed receptive fields. This paper proposes State-space Modeling and Dynamic Dual Fusion Network (SMDDFNet), a deep learning detector for traffic sign images. SMDDFNet integrates a Dynamic Dual Fusion (DDF) module and a state-space modeling backbone to enhance multi-scale feature representation. DDF combines efficient multi-scale attention with content-aware dynamic filtering in the frequency domain, while the backbone captures long-range dependencies with linear computational complexity. A multi-scale feature fusion neck further aggregates pyramid features for robust localization of small signs. Experiments on TT100K, GTSDB, PASCAL VOC, and the Roboflow~100 \emph{vehicle} subset show that SMDDFNet achieves competitive accuracy against recent detectors while retaining real-time throughput. The source code is available at https://github.com/rainbowyuyu/SMDDFNet

Explore similar work

Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Jun 1, 2026Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2Traffic Sign Recognition Autonomous Driving
Real-Time Source-Free Object Detection
Jun 30, 2026Sairam VCR, Varun Gopal, Poornima Jain +2Object Detection Real-Time Systems
DGSSM: Diffusion guided state-space models for multimodal salient object detection
Apr 19, 2026Suklav Ghosh, Arijit Sur, Pinaki MitraDense Prediction Multimodal Deep Learning

Jun 1, 2026 · cs.CVJ/K move · Enter open · S save

Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios

Mingxiao Wang, Xiaozhen Qu, Bolin Gao, Tong Wang+1

School of Automotive and Traﬀic Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121001, China. · State Key Laboratory of Intelligent Green Vehicles and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.

Traffic sign detection is a fundamental component of environmental perception in autonomous driving and intelligent transportation systems. However, most existing detectors rely on static inference with globally shared parameters, limiting their ability to adapt to diverse and unstructured traffic scenarios. As a result, a single static model often struggles to simultaneously handle both clear near-range samples and challenging conditions such as distant small targets or adverse weather environments. To address this limitation, we propose CBDES MoE TSR, a hierarchically decoupled heterogeneous mixture-of-experts(MoE) framework for traffic sign recognition. The proposed framework departs from the conventional globally shared parameter paradigm by introducing a heterogeneous You Only Look Once (YOLO) expert pool together with a lightweight gating network, enabling an image-level dynamic routing mechanism. Based on the semantic characteristics of the input image, the gating module selectively activates the most suitable expert model from the expert pool, enabling a shift from fixed parameter fitting to on-demand dynamic representation. This design enhances feature extraction capability for specific scenarios while maintaining controlled inference overhead. Experimental results demonstrate that the proposed method achieves a remarkable balance between detection accuracy and efficiency on the composite traffic sign dataset. Specifically, our method attains an mAP50-95 of 76.8%, yielding a 2.3% improvement over the baseline method (74.5%) while simultaneously reducing computational overhead by approximately 39.4%. These findings robustly validate the effectiveness of the proposed approach.

Traffic Sign Recognition Autonomous Driving Mixture-of-Experts Models Hierarchical

Open paper Ask about it PDF

Abstract

Explore similar work

Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios

Real-Time Source-Free Object Detection

DGSSM: Diffusion guided state-space models for multimodal salient object detection