Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity

TL;DR

This paper presents tailored data augmentation techniques to address class imbalance and data scarcity in traffic sign recognition, significantly improving model robustness and accuracy.

Contribution

It introduces novel augmentation methods, including obstacle-based augmentation, to enhance dataset diversity for traffic sign recognition systems.

Findings

Substantial performance improvements in TSR models

Effective augmentation methods for real-world conditions

Potential applicability to other computer vision tasks

Abstract

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.