Road Sign Recognition using Lenet5 Network Model
Suraj Singh, Bhupinder Kaur, Sumit Singh, Monika Yadav, Himanshi Sharma, Deepak Kumar Ranjan · 2021
This paper proposes a framework that will detect different types of traffic signs from images for Traffic sign recognition and detection in advanced driver support system. In this paper classical LeNet5 network model is used with GTSRB dataset for evaluation purpose. Shape and color are the two main factors that distinguish road signs from other objects. Firstly, preprocessing of an image to highlight important information. Secondly, the training data are scaled, normalized, expanded, and augmented. Finally, the validation and testing phase in which traffic signs are classified and accuracy is calculated. It is concluded from the results that our system has achieved greater accuracy due to the architectural optimization of the neural network for detecting traffic signs. Significantly better performance was achieved with partially blurred images. Traffic sign are classified in 43 classes. For training 22271 images are used in which 6960 images used for testing and 5568 images are used in the evaluation purpose. With new design model 98.75% accuracy is obtained.