Vehicle detection aimed to enhance road safety. Traffic monitoring challenges for vehicle detection and labeling. The existing methodologies are techniques that run time struggle with real-world scenarios. We applied the YOLOv5 model for vehicle detection and labeling using this model, which detects the vehicle types and labeling. In the area of Artificial Intelligence, image processing and computer vision. These are the major categories of classification for group object detection. We applied the YOLOv5 model standalone application in this paper for real-time object detection. Using this architecture, the process data configures the file and detects the objects. We have taken two scenarios: finding the sweeping vehicles and the cars labeling them. In future, it will be extended to provide an intelligent vehicle detection and monitoring.


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    Title :

    Real Time Object Detection of Vehicles and Labeling Using the YOLOv5 Method


    Contributors:


    Publication date :

    2024-12-19


    Size :

    1170428 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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