Machine Learning Approaches for Traffic Sign Detection: Methods and Challenges

Abstract

Traffic sign detection (TSD) is a fundamental perceptual task for intelligent transportation systems and autonomous driving; however, performance deteriorates in adverse weather, low light, occlusion, and cross-regional sign variability. This review summarizes how the field has changed from traditional machine-learning pipelines, such as SVM or AdaBoost with color/shape features, to modern deep architectures, such as two-stage R-CNN variants, one-stage YOLO/SSD families, lightweight models for edge deployment, and new Transformer-based detectors. This review sorts of methods into groups, compares them on major benchmarks, and look at their accuracy, runtime, and robustness. The analysis demonstrates that deep learning significantly outperforms conventional methods in terms of precision and scalability. Nevertheless, a speed-accuracy trade-off remains, and models trained within a specific sign system frequently exhibit inadequate generalization to alternative systems (e.g., India and Germany), highlighting the necessity for region-specific data or explicit domain adaptation. Ongoing problems include bad annotations and long-tail categories. The practical advice is provided for deploying on embedded platforms and highlights some promising areas to explore, such as multimodal fusion (camera + LiDAR), augmentation and adaptation for changes in weather and lighting, compact architectures with knowledge distillation, and Transformer pipelines that are optimized for small objects. The review's goal is to give a short, deployment-focused guide for improving reliable TSD in real-world ITS.