Small-Target Traffic Sign Detection Method Based on Multi-Path Feature Aggregation and Attention Enhancement

Abstract

Detecting traffic signs in real-world roadway scenes remains a demanding task due to extensive category diversity, the prevalence of diminutive targets, and interference from cluttered surroundings. To overcome these obstacles, we present YOLO-PPA, a YOLOv11n-based detector strengthened through multi-path feature aggregation and attention-enhanced representation learning. First, a Parallelized Patch-Aware Attention (PPA) mechanism is embedded in place of the standard C3K2 block, simultaneously capturing fine-grained local textures and broad contextual semantics while adaptively amplifying informative spatial regions critical for small objects. Second, a high-resolution P2 detection head is appended to the feature pyramid, recovering fine spatial cues that would otherwise be attenuated across successive downsampling stages, and this design is particularly beneficial for recognizing signage occupying only a handful of pixels. Third, the Normalized Gaussian Wasserstein Distance (NWD) replaces the conventional CIoU metric as the regression loss, offering a smoother optimization landscape for tiny instances where even single-pixel displacements can destabilize standard IoU-based objectives. Evaluated on the TT100K benchmark, YOLO-PPA surpasses the YOLOv11n baseline by 2.1% in precision, 3.7% in recall, 4.3% in mAP@50, and 3.0% in mAP@50:95, confirming its effectiveness for small-scale traffic sign recognition in complex driving environments.