Traffic Sign Recognition System using CNN


Authors : Harikesh Kumar Sharma; Anupama Jamwal

Volume/Issue : Volume 8 - 2023, Issue 11 - November

Google Scholar : https://tinyurl.com/msk7tj5f

Scribd : https://tinyurl.com/58383jt9

DOI : https://doi.org/10.5281/zenodo.10159118

Abstract : This paper shows the development and working of a traffic sign recognition system using Convolutional Neural Networks (CNNs) for the accurate detection and classification of traffic signs from images (captured by a camera mounted on a vehicle). The system achieved a high classification accuracy of 95% on a validation set and consists of three main components: image pre-processing, CNN model, and post-processing. The pre-processing step involves resizing, normalization, and augmentation, while the CNN model uses multiple convolutional and fully connected layers to classify input images into different traffic sign categories. Post- processing involves non-maximum suppression and thresholding to remove duplicate detections and filter out false positives. Testing on a real-world dataset resulted in an overall accuracy of 94.5%, demonstrating the system's robustness to varying lighting and weather conditions, as well as its ability to accurately recognize traffic signs at different distances and angles. This proposed system using CNNs is a promising solution for improving road safety and reducing accidents caused by driver error.

Keywords : Traffic Sign Recognition, Convolutional Neural Network , Traffic Sign Detection, Deep Learning.

This paper shows the development and working of a traffic sign recognition system using Convolutional Neural Networks (CNNs) for the accurate detection and classification of traffic signs from images (captured by a camera mounted on a vehicle). The system achieved a high classification accuracy of 95% on a validation set and consists of three main components: image pre-processing, CNN model, and post-processing. The pre-processing step involves resizing, normalization, and augmentation, while the CNN model uses multiple convolutional and fully connected layers to classify input images into different traffic sign categories. Post- processing involves non-maximum suppression and thresholding to remove duplicate detections and filter out false positives. Testing on a real-world dataset resulted in an overall accuracy of 94.5%, demonstrating the system's robustness to varying lighting and weather conditions, as well as its ability to accurately recognize traffic signs at different distances and angles. This proposed system using CNNs is a promising solution for improving road safety and reducing accidents caused by driver error.

Keywords : Traffic Sign Recognition, Convolutional Neural Network , Traffic Sign Detection, Deep Learning.

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