Recent Advancement in Small Traffic Sign Detection: Approaches and Dataset

Abstract

Detecting small traffic signs is essential for autonomous vehicles (AV) to operate safely, adhere to traffic regulations, navigate effectively, maintain situational awareness, adapt to local conditions, and overcome the technical challenges associated with diverse environments. As AV technology continues to evolve, improving the accuracy and reliability of small sign detection remains a crucial area of research and development. Enforcing small traffic sign detection systems (TSDS) is essential for road safety, ensuring drivers know important information, warnings, and regulations aligned with smart cities, enhancing traffic management, and optimizing signal timings. Over the past ten years, many deep-learning techniques have been reported for TSDS to identify tiny traffic regions. This review comprehensively examines the performance of state-of-the-art deep learning models, including YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and various RCNN (Region-based Convolutional Neural Network) variants, assessing their strengths and weaknesses for small traffic sign detection through detailed tables and bar graphs. Additionally, the review examines key evaluation metrics used for TSDS and explores standard benchmark datasets like TT100k, GTSDB, CCTSD, STS, and DFG. It emphasizes the dataset’s attributes and complex factors affecting detection performance. The analysis focuses on how various deep learning models perform on these standard datasets, presenting the results in tables and comparative bar graphs. Finally, the review discusses current challenges in TSDS technology and proposes recommendations for future development of driver-aid systems. This comprehensive analysis aims to guide the design of future computer vision systems for small traffic sign detection.

Keywords

• Recent advancement
• small traffic sign detection
• attention model
• semantic features
• dataset