Traffic Sign Classification Using ASIC

Abstract

Abstract: The main aim of this project is to focus on traffic signs classification using Tensor flow and deploying the model onto an ASIC for engineering applications. The classification is done using Convolutional Neural Networks (CNNs) in TensorFlow, where the model is trained using a large dataset of traffic sign images. The training images are pre-processed and labelled, and the model is fine-tuned using transfer learning techniques. After the model is trained, it is tested with a separate dataset to ensure its accuracy. Once the model is finalized, it is optimized for deployment onto an ASIC using Micro Python. The ASIC is a custom-designed hardware that is optimized for the specific task of traffic sign classification. The performance of the ASIC is then compared with a standard computer to evaluate its speed and efficiency. This project demonstrates the ability to implement machine learning algorithms onto custom-designed hardware for specific engineering applications, specifically for the purpose of traffic sign classification.