Moving Vehicles Counting and Detection Using Deep Neural Networks Based YOLO-NAS Algorithm

The precision and effectiveness of difficult tasks like object detection, tracking, and classification have been greatly improved by deep learning. The capacity of deep learning models, specifically convolutional neural networks (CNNs) and transformer-based architectures, to handle hierarchical representations has made them suitable for use in real-time scenarios such as vehicle detection and traffic monitoring. In this work, we apply a sophisticated object detection model that makes use of neural architecture search to achieve optimal performance using the YOLO NAS (You Only Look Once Neural Architecture Search) method. Because it can process images in a single pass, YOLO NAS is more precise and faster than its predecessors, which makes it ideal for real-time applications. Finding, counting, and classifying automobiles into categories like “Car” and “Truck” is the aim of this work. In addition, the computer classifies the vehicles based on their estimated velocities. In future implementations, integrating additional features like pedestrian detection or anomaly recognition could further enhance its utility for urban planning and smart city initiatives. The continuous collection of data through real-time monitoring also enables the development of predictive models for traffic flow and congestion management, which can significantly impact sustainability and safety efforts in large metropolitan areas. By striking a balance between speed and accuracy, the usage of YOLO NAS aids in traffic analysis and urban planning. Reliability in vehicle identification, categorization, and speed estimation in various contexts is guaranteed by the model's performance, which can achieve up to 92% accuracy based on the methodology and dataset used. For traffic management and accident avoidance, these insights offer useful real-time data.