Abstract
Traffic sign classification automatically detects roadside traffic signs, such as speed limit signs, yield signs, etc. Automatically recognizing traffic signs enables the development of “smarter automobiles.” Self-driving automobiles require traffic sign recognition to interpret and comprehend the roadway accurately. Similarly, “driver alert” systems within cars must understand the surrounding roadway to assist and protect drivers. Our automation would assist drivers in detecting and identifying traffic signs without distracting them from the road. With convolution neural networks, the signboards can be accurately classified. The precision can be improved by adding more layers. The GTSRB dataset is utilized here for training and testing; by fine-tuning the parameters, the 43 types of traffic signs are categorized accurately, and the detection speed also increases.
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