Traffic Sign board image Classification by using Deep Learning Techniques

Author:

Deshpande Abhinav V.1

Affiliation:

1. Vellore Institute of Technology (VIT)

Abstract

Abstract In this research paper, the comparison of various regional image enhancement methods is made with three-dimensional image quality statistics such as mean error (MSE), peak signal-to-noise ratio (PSNR), and image quality. (SSIM) Image quality. Image development activities can be carried out, especially in local regions. The criteria for choosing the best method are the characteristics of the mean square error (MSE) and cumulative mean square error (MSE) to noise ratio (PSNR), as well as the maximum measurement and image quality (SSIM). Classification of tasks from preliminary images using the pre-learning technique of deep convolutional neural network. Accuracy for testing and training image databases is 100%.

Publisher

Research Square Platform LLC

Reference8 articles.

1. Traffic sign detection and recognition based on random forests;Ayoub Ellahyani ME;Elsevier- Appl Soft Comput

2. Real-time traffic sign recognition in three stages;Fatin Zaklouta B;Elsevier- Rob Auton Syst

3. Traffic sign recognition using group sparse coding;Huaping Liu Y;Elsevier- Inform Sci

4. Driver behavior during bicycle passing maneuvers in response to aShare the Road sign treatment;Jonathan J;Elsevier-Accident Anal Prev

5. Traffic sign detection via interest region extraction;Samuele Salti A;Elsevier- Pattern Recognit

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