The application of computer vision techniques has brought traffic safety analysis more advancement by allowing researchers to study traffic conflicts from vehicle data. Traffic management systems capture video data and leverage advances in video processing to detect and monitor trafficincidents.
In this work, we design, implement, and analyze a computing model for smart traffic monitoring system. Moreover we can use the concept of smart traffic in traffic analysis and congestion prediction, road safety and accident prevention and AI-based traffic management systems.
YOLO-NAS is a foundation model for object detection.Itimproves small object detection, localization accuracy, andperformance-per-compute ratio. The “NAS” is used to automate the design process of neural network architectures. Instead of relying on manual design, we use NAS algorithms to discover the most suitable architecture for a given task. The aim of “NAS” is to find a model that achieves best accuracy, computational cost, and optimized model.
The following prototype processes a series of images captured by cameras or videos. It makes predictions on each image, identifyingand localizing region of interest ROI. Detected objects belonging to classesare filtered based on a predefined confidence threshold (e.g., confidence >=0.6) to ensure reliability.
By utilizing the capabilities of object detection models andadvanced analytics there is a potential to revolutionize urban mobility andenhance road safety in the years to come.
مؤسسة عرين الأسد للتسويق الإلكتروني
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