TRAFFIC SIGN DETECTION AND RECOGNITION using python

COMPUTER SCIENCE AND ENGINEERING

This paper is focused on the development of a convolutional neural network (CNN)-based model for traffic sign detection and recognition. The increase in automobile ownership and traffic has made it challenging for drivers to accurately identify traffic signs, leading to an increased risk of accidents, loss of life, and property damage. To address this issue, an intelligent traffic sign detector and recognizer is required. Our proposed CNN-based model is designed to identify traffic signs accurately and effectively, with a success rate of approximately 97%. The model's accuracy is achieved by using a deep learning approach that is capable of learning and recognizing the features of traffic signs. This approach involves training the model on a large dataset of traffic signs, which enables it to identify traffic signs in real-world situations. Through our research and experimentation, we demonstrate the efficacy and potential impact of this model on road safety. By accurately detecting and recognizing traffic signs, our model can help reduce the likelihood of accidents and their associated costs. Our findings suggest that our CNN-based model can be a valuable tool for improving road safety, ultimately helping to save lives and prevent property damage. In summary, our thesis presents a CNN-based model for traffic sign detection and recognition, which can accurately identify traffic signs with a success rate of approximately 97%. Our research demonstrates the potential impact of this model on road safety and suggests that it can be an effective tool for reducing the likelihood of accidents and their associated costs.

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