Traffic sign detection is a critical component in advancing intelligent transportation systems and ensuring road safety. Convolutional Neural Networks (CNNs) have emerged as powerful tools in image processing and computer vision, demonstrating remarkable success in various domains, including traffic sign recognition. This paper presents a comprehensive exploration of the challenges associated with traffic sign detection and the application of CNN techniques to address these issues. The paper reviews the traditional methods used for traffic sign detection and emphasizes the limitations that motivate the adoption of CNNs. The core of the paper delves into the application of convolutional neural techniques for traffic sign detection. Various CNN architectures are surveyed and highlight their strengths and weaknesses in handling specific challenges inherent to traffic sign recognition. Attention mechanisms, transfer learning, and other advancements in CNNs are explored for their potential to enhance detection accuracy and robustness. In conclusion, this paper contributes to the understanding of how convolutional neural techniques can be leveraged to tackle traffic sign detection challenges. © 2024 Elsevier B.V., All rights reserved.