mlsanityData to 14 Oct 2026

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

arXiv 2505.05491 ↗First posted 2 May 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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 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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 63 references that are in the map. First posted on arXiv 2 May 2025.

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43 of 63 references.

References · 43

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