Traffic Sign Recognition Method Based on Convolutional Neural Network

Traffic sign recognition constitutes a crucial component in the realm of autonomous driving. To address the issue of sub-optimal recognition accuracy, we propose an innovative method premised on convolutional neural networks. This method enhances recognition accuracy through data augmentation and the refinement of deep learning networks, thereby successfully recognizing traffic signs. The enhanced model underwent training and validation on the German Traffic Sign Recognition Benchmark (GTSRB), yielding a recognition accuracy of 97.76% on the test set. The experimental results illustrate that our approach can effectively and accurately recognize traffic signs in complex environments, rendering it a valuable technical asset for subsequent research in this field.