Traffic sign recognition algorithm based on improved ResNet18

With the continuous development of transportation systems and the rise of autonomous driving, the recognition of road traffic signs is becoming increasingly important in the field of intelligent transportation. The recognition of traffic signs requires higher accuracy and faster speed, which also imposes higher requirements on traffic sign recognition models. Currently, most research tends to focus on higher accuracy, lacking comparisons in model speed. Although most researchers have recognized the good results of training traffic sign recognition models using convolutional networks, they have overlooked the application of the ResNet18 model in traffic sign image recognition. Based on this fact, this paper focuses on constructing and improving the ResNet18 network model and training and evaluating it based on GTSRB, aiming to improve model speed while ensuring high accuracy. After multiple experiments, the accuracy of the recognition model reached 99.60%, with a speed of recognizing each image reaching 0.26ms. Comparative experiments with models such as Single-linkage+CNN and VGG16 on the GTSRB dataset validated the performance advantages of the improved model proposed in this paper (ResNet18_final model).