Deep CNN Model for Condition Monitoring of Road Traffic: An Application Of Computer Vision - Reddy, Dr. P. Hasitha; Manjunath, M.; Rohith, M.; Reddy, N. Manohar; Satyanarayana, A. (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2024)
The traffic surveillance system accumulates an enormous amount of data regarding road traffic each second. Monitoring these data with the human eye is a tedious process and it also requires manpower for monitoring. Deep learning Convolutional Neural Network (DLCNN) can be utilized for traffic monitoring and control. The traffic surveillance data are preprocessed to construct the training dataset. The Traffic net is constructed by transferring the network to traffic applications and retraining it with a self-established data set. This Traffic-net can be used for regional detection in large scale applications. Further, it can be implemented across-the-board. Further, DLCNN is used for prediction of traffic status i.e., dense traffic, low traffic, accident, and fire occurred from test sample. Finally, the simulations revealed that the proposed DLCNN resulted in superior performance as compared to existing model.