Traffic Density Estimation System using Deep Learning Technique for Vehicle Detection

Abstract

Nowadays driving a car is necessary to travel to places. The benefits of a car to travel are comfortable and fast. Since the increasing number of cars are being driven every day, the number of on-site parking spaces is insufficient to park cars. Therefore, this work is created to help us know how many parking spaces are left inside the facility. The equipment used in the experiment was a camera and a laptop. The camera was installed at an electricity post near the dormitory. After the video of cars entering and exiting the road have been recorded, they were processed by YOLOv4 techniques. The images of the cars were analyzed. Which has been experimented with two forms: The first form is a separate type of car. The result is sedan car has the highest detection accuracy of 100%, while the van has the lowest detection accuracy of 66.7%. The second form was the accuracy of vehicle detection at times intervals. The result of the highest detection accuracy is 94.8% in the evening and the lowest detection accuracy is 68.4% at night. Finally, the number of cars entering and exiting has been counted automatically and stored in the database. Not only the number of cars entering, exiting, and remaining in a place have been shown on the web page in real-time, but also the statistics of the total number of cars that enter each day has been stored. This project will help to make it easier to see how many parking spaces are left to be able to park. Which our web page, the part that received the most satisfaction score is contact at an excellent level.