Traffic sign recognition plays an important role in driver assistance systems for traffic safety. Convolutional neural networks (CNNs) have made a significant breakthrough in computer vision tasks and achieved considerable success in traffic sign detection and recognition. However, existing methods typically fail at achieving real-time recognition. Therefore, this study proposes a modified traffic sign recognition method based on a CNN, wherein inception modules are added, the network structure is extended, and a new loss function is used to overcome the original model's difficulty in detecting small targets. German traffic sign datasets are used to simulate the effectiveness of the proposed method. Simulation results show that the proposed method can obtain higher detection rates than those of existing methods at the processing time of only 0.015 s for each image.