aiqbench analysis
This paper introduces SMDDFNet, a deep learning architecture leveraging state-space modeling and dynamic dual fusion to improve traffic sign detection in autonomous driving scenarios. By addressing challenges like small object scale variation and occlusion with linear computational complexity, the model offers a viable path toward robust real-time perception systems for advanced driver assistance.
SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection
arXiv:2505.05491v2 Announce Type: replace-cross 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
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 this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2505.05491 [cs.CV]
(or arXiv:2505.05491v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2505.05491 Focus to learn more arXiv-issued DOI via DataCite
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