<p>Automatic traffic sign detection is a key capability for intelligent vehicles and a fundamental component in the smart management of road infrastructure and traffic maintenance systems. Accurate, large-scale monitoring in real-world conditions requires models that balance high performance with computational efficiency. This study presents a lightweight, deployable version of YOLOv8-n, trained using a multi-source data augmentation strategy to support applications such as surveillance networks and intelligent transportation systems. To improve spatial, contextual, and illumination diversity in the training data, three independent augmentation methods were applied: (1) structured cropping of regions of interest (ROIs), (2) synthetic image generation using varied background scenes, and (3) the Copy-Paste technique incorporating real traffic sign instances. These augmented datasets were incrementally combined with real-world images from the DFG dataset to construct a diverse and realistic training corpus. All models were trained using an input resolution of 640 × 640 pixels. Extensive evaluations on five distinct validation datasets showed that the proposed model achieved over 90% mAP@50 (mean Average Precision at an Intersection over Union threshold of 50%) accuracy in most scenarios, including out-of-distribution conditions. With its low computational cost and efficient inference, the model is well-suited for real-time applications such as traffic sign condition monitoring, infrastructure maintenance planning, and road safety improvement. These findings support the development of cost-effective, accurate, and scalable solutions for intelligent traffic management systems.</p>