Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.
Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia.
Sensors (Basel). 2023 May 11;23(10):4674. doi: 10.3390/s23104674.
Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.
由于汽车和计算机视觉技术的快速发展，自动驾驶汽车已经成为当前的热门话题。自动驾驶汽车安全高效行驶的能力在很大程度上依赖于其准确识别交通标志的能力。因此，交通标志识别成为自动驾驶系统的关键组成部分。为了应对这一挑战，研究人员一直在探索各种交通标志识别方法，包括机器学习和深度学习。尽管做出了这些努力，但不同地理区域、复杂背景场景和光照变化下交通标志的多样性仍然对可靠的交通标志识别系统的发展构成了重大挑战。本文全面概述了交通标志识别领域的最新进展，涵盖了预处理技术、特征提取方法、分类技术、数据集和性能评估等各个关键领域。本文还深入探讨了常用的交通标志识别数据集及其相关挑战。此外，本文还揭示了交通标志识别的局限性和未来的研究前景。
