Vol. 8, Issue 2 (2019)

Author(s):
Dr. Neeraj Sharma

Abstract:
This research paper presents a system for vehicle detection and classification using Open CV and YOLO. The system is capable of accurately counting the number of vehicles in both incoming and outgoing lanes and classifying them into three categories: cars, trucks, and buses. The system utilizes object detection to detect each vehicle as it enters the frame and then tracks it through the video stream to count its movement direction. Additionally, a YOLO model was trained on a dataset of images containing the three vehicle categories, enabling the system to classify each detected vehicle. The proposed system was evaluated on several video datasets, demonstrating high accuracy in vehicle detection and classification. This research paper contributes to the field of computer vision by presenting a robust and efficient system for real-time vehicle detection and classification, with potential applications in traffic monitoring and smart transportation systems.