Intelligent Traffic Light Control System using Caffe Model: A Deep Learning Strategy

Due to the development of urban areas, there's a considerable increase in the population that powerfully enforces the necessity of advanced technology to observe the daily road traffic of vehicles. There has been an enormous increase in traffic in every part of the town as a result of the rapid population migration. This contributes to the loss of valuable time and fuel, which causes people to lose patience and get frustrated. Researchers have developed many methods for dealing with this issue. Researchers have developed many methods for dealing with this issue. The timer model is one of the models that is currently in use. Timer usage at all places along the route is frequently used to greatly regulate traffic. Another variant in use includes electronic sensors to detect the presence of vehicles and provide the appropriate signals. However, both of these models cannot be effective owing to the serious density of vehicles. By applying image processing to construct a density-based traffic control system that adjusts the traffic lights based on the number of cars continuously, this limitation can be greatly reduced. This process uses image processing techniques and deep neural networks so as to determine how many vehicles are currently on the road (traffic density). The image processing functions in OpenCV and Caffe model in deep learning can be used to set up the code that counts the number of vehicles that are close to the signal. The system is extremely light-weight in design thereby consuming less memory and working at high accuracy of ninety five percent in detection objects.