A standalone ATCC system based on neural network/map matching technique by implementing YOLOv5 based CNN.
Collecting real-time, reliable, and precise vehicle flow information is crucial for instant management of traffic on roads. To maximize the capacity of city roads as well as highways, continuous vehicle monitoring, counting, and classification efforts need to be carried out to understand seasonal, day-of-the-week, and time-of-the-day traffic volume patterns. Automatic Traffic Counter and Classifier (ATCC) monitors the real-time traffic flow of a road section, keeps count of vehicles, and classify them according to their pre-defined classes.
YOLO is an acronym that stands for You Only Look Once. We are employing Version 5, which was launched by Ultralytics in June 2020 and is now the most advanced object identification algorithm available. It is a novel convolutional neural network (CNN) that detects objects in real-time with great accuracy. This approach uses a single neural network to process the entire picture, then separates it into parts and predicts bounding boxes and probabilities for each component. These bounding boxes are weighted by the expected probability. The method “just looks once” at the image in the sense that it makes predictions after only one forward propagation run through the neural network. It then delivers detected items after non-max suppression (which ensures that the object detection algorithm only identifies each object once).
- Free flow traffic count and classification, operates 24X7
- Cover up to four lanes of traffic
- Neural Network based classification
- Fully customized reporting system to meet unique business requirements
- Applications across traffic signal design, toll enforcement/free-flow tolling, infrastructure planning, violation detection (vehicle coming from the opposite direction)
- Low false classification rate
