  This paper tackles critical challenges in traffic sign recognition (TSR),
which is essential for road safety -- specifically, class imbalance and
instance scarcity in datasets. We introduce tailored data augmentation
techniques, including synthetic image generation, geometric transformations,
and a novel obstacle-based augmentation method to enhance dataset quality for
improved model robustness and accuracy. Our methodology incorporates diverse
augmentation processes to accurately simulate real-world conditions, thereby
expanding the training data's variety and representativeness. Our findings
demonstrate substantial improvements in TSR models performance, offering
significant implications for traffic sign recognition systems. This research
not only addresses dataset limitations in TSR but also proposes a model for
similar challenges across different regions and applications, marking a step
forward in the field of computer vision and traffic sign recognition systems.
