We propose a deep learning model training on GPU nvidea 1060ti 6gb to detect,count vehicles and control one way traffic lights.The proposed model is be composed of several components to improve the performance such as congestion control and automatic street lights using sensors and arduino to integr
Artificial Intelligence based Smart traffic monitoring system and automatic street lights
We propose a deep learning model training on GPU nvidea 1060ti 6gb to detect,count vehicles and control one way traffic lights.The proposed model is be composed of several components to improve the performance such as congestion control and automatic street lights using sensors and arduino to integrate whole setup.Our developed solution is targeted to incorporate the progress in the deep learning where low power embedded devices integrate as part of next generation Traffic management system.
1).Counting of cars using deep learning models and performing several functions upon counted cars according to situation of congestion.
2).Automatic street lights for power saving purpose.
1).First training of model and counted cars through object detection using CNN has been performed
using 1060ti 6gb.
2).Performed checks on stored counting vehicle variable for operating of signals.
3).Inetgration of arduino and trained model using camera.
4).Integration of both models for 1-way intersections.
5).Using wood model and embedding automatic street lights using sensors.
1).Reduce day-to-day congestion by improving traffic flow.
2).Provide data for further improvement in transportation.
3).Prioritize traffic according to real time changes in traffic conditions.
4).Reduce pollution by limiting traffic jams.
5).Saving energy by automation of highway lights.
This system is designed to govern traffic at road networks, sensing through cameras by means of Deep Learning, surveillance IR sensor which are embedded on roadsides in order to save energy on highway lights.The system works in distributed manner, it processes the cameras that operates the signals control.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| GPU | Equipment | 1 | 55000 | 55000 |
| Arduino | Equipment | 4 | 800 | 3200 |
| Bulb | Equipment | 4 | 180 | 720 |
| Holder | Equipment | 4 | 120 | 480 |
| White board | Equipment | 1 | 1000 | 1000 |
| Board, Wires | Equipment | 1 | 1100 | 1100 |
| Camera | Equipment | 2 | 3000 | 6000 |
| IR sensor | Equipment | 10 | 250 | 2500 |
| Miscellaneous items | Miscellaneous | 1 | 10000 | 10000 |
| Total in (Rs) | 80000 |
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