Traffic sign detection and recognition based on convolutional neural network

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately.  To develop an efficient traffic sign detection and recognition that can detect and categorize traffic signal into different classes in real-time with the help of Deep Learning techniques. In this project, regard traffic sign detection as a region classification problem and propose a two-stage R- CNN-based approach to solve it.  To build a deep neural Network model that can classify traffic sign present in images into different categories. The training process takes predefined traffic signals and has them 'learn' into a model.  With this model, people are able to read and understand traffic signs which are a very important task for all autonomous vehicles. A visual traffic sign recognition system can be integrated into the automobile with the objective of detecting and recognizing all emerging traffic signs. In case the driver refuses to heed traffic signs, the system will trigger an alarm.&nbsp