Advanced Road Sign Detection System Utilizing Convolutional Neural Networks

This paper analyses how artificial intelligence may enhance the recognition of traffic signs, which is essential for enhanced driver support systems and autonomous vehicles. Based on benchmark datasets and real-world testing, key findings show a highly accurate and efficient road sign detection system that can identify different signs under varying illumination and weather scenarios. Road sign characteristics, detecting challenges, outdoor image processing, color and shape-based segmentation, and sign identification methods are all covered in the paper. It emphasizes difficulties with lighting deviation, color stability, and the lack of a strong color model, among other issues this demands for evolution. CNN With the potential for innovative techniques to improve durability and real-time capabilities, neural networks are frequently utilized for detection and recognition tasks. Our model a significant accuracy of 98.88 % making it a efficient for real word application.