Transfer Learning Based Traffic Sign Recognition System for Surveillance Application
Manisha Chahande1, Vinaya Gohokar2
1Manisha Chahande, Amity University ,Noida, India.
2Vinaya Gohokar, Maharashtra Institute of Technology ,Pune, India.
Manuscript received on 8 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 573-576 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2025078219/19©BEIESP | DOI: 10.35940/ijrte.B2025.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Road Traffic Recognition is very important in many applications, such as automated deployment, traffic mapping, and vehicle tracking. Proposed traffic sign recognition system tails the transfer learning method that is frequently used in neural network uses. The benefit of expending this technique is that the initially network has been trained with a rich set of features appropriate to a wide range of images. Once the network is trained , learning can be transferred to the new activity adjustment to the network. Firsthand Indian traffic sign dataset is used. New results exhibit that the suggested method can accomplish modest outcomes when matched with other related techniques.
Index Terms: Convolution Neural Network, Object Detection, Traffic Sign Detect, Transfer Learning, Driver Assistance System
Scope of the Article: Convolution Neural Network