A Traffic Sign Detection Algorithm Based on an Improved YOLOv8n

To address the limitations of YOLOv8n in multi-scale feature representation and high false negative rates for small traffic signs under edge-computing constraints, this paper proposes an improved lightweight detection algorithm integrating the VoVGSCSP module and a Multi-scale Contextual Attention (MCA) mechanism. Specifically, the original C2f module is replaced with VoVGSCSP to enhance gradient flow and aggregate multi-scale receptive fields, while MCA captures discriminative shape, boundary, and color features via multi-branch pooling with dynamic weight fusion. The PAN-FPN is further optimized using Learnable Weight Concatenation (LWConcat) for adaptive multi-level feature fusion. On the CTSDB dataset, the proposed model reduces parameter count to 2.90 M (4.0% reduction) and FLOPs to 7.4 G (8.6% reduction), while improving mAP0.5 from 96.2% to 99.4% and mAP0.5:0.95 from 94.8% to 98.6%. On the TT100K dataset, mAP0.5 increases from 60.2% to 61.9% and mAP0.5:0.95 from 44.9% to 46.5%. The smaller improvement on TT100K suggests greater dataset diversity and annotation complexity, indicating a direction for future work. Overall, the proposed algorithm achieves a favorable trade-off among accuracy, model size, and computational cost, validating its practicality for resource-constrained edge deployment.