Traffic Sign Classification using Le-Net Architecture

Roadside traffic signs are crucial to our safety while we are travelling. Similar to human drivers, classification of traffic signs is a crucial component in self-driving car applications. Self-driving cars must use camera data to make judgments based on what they observe. Self-driving cars must be able to recognize things and classify traffic signs in order for us to know when they cease yielding and if they will go at a speed of 30 or 50 kilometers per hour. To categorize traffic signs, utilize the traffic sign classification system. so that they can be informed and warned in advance to prevent rule violations. The suggested approach substantially addresses certain drawbacks of the current classification systems, such as inaccurate predictions, hardware cost, and maintenance. The suggested method uses LE-NET architecture to provide a categorization of traffic signs. This will enable autonomous vehicles to decide depending on what they perceive.