Optimizing Traffic Sign Detection and Recognition by Using Deep Learning

Enhancing performance standards by judiciously fusing established methods with innovative strategies. This paper aims to combine the existing YOLOv5 algorithm, which is well-known for its object identification abilities, with new models, such as the Autoencoder-CNN (Convolutional Neural Network), Autoencoder-LSTM (Long Short-Term Memory), and Recurrent Neural Network (RNN) frameworks, in order to improve its performance. Through combining these disparate methods, the study seeks to use each of their unique advantages, ultimately resulting in a thorough comparison study that reveals their separate effects on precision and productivity. This methodical assessment, characterized by rigorous optimization and careful testing, not only improves traffic sign recognition systems' accuracy but also reveals useful connections between the suggested and known methods. The main goal of this endeavor is to unravel how these seemingly unrelated components, when brought together, can potentially usher in a new age of higher performance standards. This study aims to pave the way for the development of more sophisticated, flexible, and well-tuned traffic sign detection and identification systems by bridging the gap between the established and the cutting edge. The ramifications of this work encompass a wide range of real-world applications. Robust optimization and experimentation not only improve traffic sign recognition systems' accuracy but also reveal useful connections between the suggested and proven methods.