MCSCA: Multi-dimensional Collaborative Spatial-Channel Attention Network for Traffic Sign Recognition

Abstract

Traffic sign recognition is a safety-critical perception task in intelligent transportation systems, requiring accurate classification under complex real-world conditions including illumination variation, viewpoint changes, motion blur, and environmental degradation. Existing methods often rely on single-branch attention mechanisms that capture only partial feature dependencies, limiting robustness under degraded visual conditions. To address these limitations, we propose MCSCA, a Multi-dimensional Collaborative Spatial-Channel Attention network that integrates three complementary attention branches—Neuron Saliency Enhancement (NSE), Spatial-Channel Collaborative Calibration (SCC), and Cross-Dimensional Interaction (CDI)—through a learnable Softmax-weighted adaptive fusion strategy. The three branches operate in parallel on shared intermediate feature maps, simultaneously enhancing neuron-level saliency, spatial-channel contextual dependency, and cross-dimensional structural interaction. The fused representation is further stabilized via residual connection. The proposed model is built upon a lightweight residual backbone with multi-scale feature aggregation and is trained using AdamW with warmup-cosine scheduling, CutMix/Mixup augmentation, and label smoothing. Experiments on GTSRB demonstrate that MCSCA achieves 99.89\% validation accuracy, 99.97\% precision, and 99.80\% recall at 633.4 FPS with only 4.38M parameters, maintaining competitive performance while preserving real-time inference efficiency. Robustness evaluation on GTSRB-C, a corrupted benchmark covering 8 camera corruption types at 5 severity levels, shows a mean corruption accuracy (mCA) of 81.81\% and a composite RobScore of 76.91, with near-perfect robustness under photometric corruptions (Fog mCA: 99.85\%, Rain mCA: 98.87\%) and graceful degradation under additive noise and motion blur. These results validate the effectiveness of the proposed multi-branch collaborative attention design for robust traffic sign recognition under real-world perturbations.