Traffic Sign Detection Combining Frequency-Domain Enhancement and Local Channel Attention

Abstract

Traffic sign detection represents a critical visual perception task in intelligent transportation systems and autonomous driving technologies, where accurate detection directly impacts driving safety. However, existing methods still face two major challenges in practical deployment: difficulty in small object detection and insufficient adaptability to complex environments. To address these issues, this paper proposes a traffic sign detection method integrating frequency domain enhancement and local channel attention based on RT-DETR. First, the backbone network structure is optimized by incorporating Cross Stage Partial (CSP) connections, achieving local-global-frequency domain three-dimensional collaborative feature extraction while maintaining comparable parameter count and computational overhead. Second, we design the EVCGLU (Enhanced Vision Convolutional Gated Linear Unit) module, which sequentially stacks five core components along the residual connection pathway: 3×3 Depthwise Separable Convolution (DWConv), Hidden State Mixer-based State Space Duality (HSM-SSD), Layer Normalization (LayerNorm), 3×3 Depthwise Separable Convolution, and Convolutional Gated Linear Unit (CGLU). This architecture implements local feature-based channel attention, effectively enhancing model robustness. Experimental results on the TT100K dataset demonstrate that the proposed method achieves mAP@0.5 of 83.1% and mAP@0.5-0.95 of 64.7%, representing improvements of 0.2 and 0.7 percentage points over the baseline RT-DETR-R18, respectively. The parameter count is merely 14.54M with computational cost of 48.1G FLOPs, reducing by 27.0% and 15.8% compared to RT-DETR-R18. The inference speed reaches 96 FPS, satisfying the real-time requirements of onboard embedded devices.