Improvement of Vehicle detection and classification performance with Region of Interest

This Abstract-Paper presents an innovative approach to improve the performance of vehicle detection and classification in context computer vision. study proposes use Region Interest (ROI) identify process information traffic images. The proposed method is based on a combination deep learning-based object algorithms with more focused ROI-based approach. First, we state-of-the-art convolution neural networks perform images, which involves visual recognition locations. Then, using ROI, reorder reduce dimensions irrelevant data remove background areas increase attention objects. aims computational load speed up processes. We evaluate wide dataset compare results conventional approaches without ROI. experimental show that outperforms terms accuracy. In conclusion, this succeeded improving by leveraging Interest, allows better focus relevant regions reduces complexity. These can make important contribution development efficient reliable control transportation system.