GitHub / nikhilpatil99 / Smart-Traffic-Management-Using-Deep-Learning

The traffic handling schemes that are in use today are fixed time allocated traffic signal which do not change on incoming traffic or fail to provide time allocation scheme over changing traffic. A solution is required to the traditional traffic signal problem. Thus there is need to make smart traffic control system which can identify types of vehicles in a video frame belonging to categories of car, truck, bikes and buses along with number of vehicles present to control traffic by adjusting traffic signal timing for each individual lane and send this data to its connected signals and alert them of incoming traffic to calculate respective time allocation for each individual lane by using deep learning algorithms and object detection. In this work vehicles are categorized into different class such as car, truck, bike, and bus based on our own dataset which contains labeled image dataset. This classification and object detection model can be used for traffic detection, vehicle detection and other respective fields of vehicle detection.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nikhilpatil99%2FSmart-Traffic-Management-Using-Deep-Learning

Topics: cnn-model, opencv2, python3, sql-database, tensorflow

Files