A Hybrid Approach for Vehicle Detection and Counting Using Machine Learning Algorithms

The research work introduces an ML algorithm-based hybrid method for vehicle recognition and counting. The suggested approach seeks to provide reliable and effective performance in traffic monitoring applications by using both contemporary machine learning models and conventional computer vision methods. CNNs are used for feature extraction after pre-processing stages for picture augmentation to reliably detect cars. Within the established areas of interest (ROIs), cars are then classified and localized using region-based localization and object identification algorithms. In-depth research of traffic patterns is also made possible by algorithms for monitoring and counting vehicles. The successful detection and counting of cars in a variety of environmental situations is shown by the experimental findings. In terms of accuracy and processing efficiency, comparative analyses demonstrate how much better the hybrid approach is than current techniques. In addition to providing new approaches for traffic management, urban planning, and road safety measures, this study advances intelligent transportation systems.