CNN-based Traffic Sign Recognition System has been a key technology in intelligent transportation and advanced driver assistance systems of today. In particular, a Recognition System uses convolutional neural networks to identify and categorize road signs from images. As a result, this system enhances sign detection accuracy, improves safety, supports autonomous navigation, and promotes following traffic rules.
The architecture of a Traffic Sign Recognition System with CNN includes several steps: image acquisition, preprocessing, classification, and post-processing. Cameras capture images that are normalized and resized. The CNN learns from this data, often using transfer learning for efficiency. Datasets, such as the GTSRB, provide labeled examples for training, which should be balanced. Performance is measured by accuracy and precision, and deployment must consider hardware limits and safety regulations.
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