Smart traffic management system using Deep Learning
Traffic Signal Detection and Counter using Machine Learning, In this project we used Yolo Algorithm for vehicle detection, Once user execute the code then user have to pass input image and model will detect and stored in separate folder. Python programming language is used to develop that project. This model will detect and identify the vehicles and make a green boundary box. There is a Traffic Control System with Traffic light, it will detect the vehicle and count the number of vehicles passing through it. Traffic light work according to traffic and number of vehicles.
Smart Traffic Managment System, This project is about managing traffic from each lane according to traffic light timer. This system will detect vehicle and count total number of vehicle and classify type of vehicle like normal or emergency vehicle. We used YOLO Algorithm to detect vehicle. After detection of vehicle model will count the total number of vehicle and allocate time according to required. Time will allocate according to total number of vehicles in each lane
Drawback of Current Traffic Signal Counter System:-
1. On traffic signal in each lane there is private vehicle and in one lane there is emergency vehicle like ambulance then in current system, ambulance have to wait for 60 second or 90 second for passing each lane.
2. Considering 1st lane have 5 vehicle and 2nd lane contain 10 vehicle and so on, then the current system will gave equal time i.e, 30 second for each lane that is drawback of current system. 1st lane won’t required 30 second because it contain only 5 vehicle that require 10 second.
Traffic Control Management System Project
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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