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Traffic Sign Recognition Using Small-Scale Convolutional Neural Network

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Document pages: 10 pages

Abstract: Traffic sign recognition has been of utmost importance ever since the emergence of the need for autonomous vehicles and driver assistance systems. An effective pre-processing of the data is important in the autonomous driving system. There is no scope to apply complex transformations or highly computational image processing techniques for such real-time purposes. This work presents an approach to recognize traffic signs using small-scale deep convolutional neural networks (CNN) and that can be applied to different applications. The presented solution is implemented using the German Traffic Sign Recognition Benchmark (GTSRB) dataset. This dataset is reliable, vibrant, and has been used for the training of different systems. The proposed system is an Advanced Driver Assistance System (ADAS) based solution to provide effective assistance. The achieved testing, training and validation accuracies are 97.71 , 99.19 , and 99.61 respectively.

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