Traffic sign recognition systems have been introduced to overcome road-safety concerns. These systems are widely adopted by automotive industry whereby safety critical systems are developed for car manufacturers. To develop an automatic TSDR system is a tedious job given the continuous changes in the environment and lighting conditions. Among the other issues that also need to be addressed are partial obscuring, multiple traffic signs appearing at a single time, and blurring and fading of traffic signs, which can also create problem for the detection purpose . For applying the TSDR system in real-time environment, a fast algorithm is needed. As well as dealing with these issues, a recognition system should also avoid erroneous recognition of no signs. TSDR system would detect and classify a collection of 43 individual traffic-signs taken from real-time environment into different classes for recognition. In this project classification of individual traffic signs is done using deep Convolutional Neural Network with VGG-net architecture model to develop an efficient classifier with improved prediction accuracy (using GTSRB dataset).
| Identifer | oai:union.ndltd.org:lmu.edu/oai:digitalcommons.lmu.edu:etd-2123 |
| Date | 01 May 2022 |
| Creators | Kanagaraj, Kanimozhi |
| Publisher | Digital Commons at Loyola Marymount University and Loyola Law School |
| Source Sets | Loyola Marymount University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | LMU/LLS Theses and Dissertations |
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