Affiliation:
1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
2. Centre for Cyber-Physical Systems and School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
Abstract
In smart cities, road accidents are very serious and avoidable. In addition, road accidents are very common in India, and they are also becoming a serious issue all over the world. In this world, the people are not able to reach hospitals due to the traffic, lack of transport facility, and unavailability of hospitals after the accidents. To enrich the population of urban people, the “smart cities” are developed to enhance the sophistication in daily life through technological development. The proposed image classifier model has been tuned with different values of hyperparameters such as number of units, activation function, optimizer, learning rate, and number of epochs. The efficiency and accuracy of the model is duly considered while building the model for predictive analysis. The images were transformed and augmented before feeding them into the neural network to ensure proper training by blocking over-fitting of the model because of lack of data. The proposed model achieves 98.21% accuracy, which is greater than the existing works.