Accurate and robust traffic sign detection is crucial for the development of advanced driver-assistance systems (ADAS) and autonomous vehicles. This paper presents a review of recent advancements in intelligent traffic sign identification using Convolutional Neural Networks (CNNs). The article surveys various CNN-based architectures, methodologies, and optimizations employed to address the challenges of traffic sign detection, including variations in illumination, weather conditions, and sign degradation. The performance and limitations of current approaches, along with potential future research directions, are discussed.

Convolutional Neural Network, Traffic Sign Recognition, Intelligent Transportation Systems

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