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@article{178297,
        author = {Mr. P. MANGA RAO and B. Prashanth and D. Naveen Kumar and A. Upendar},
        title = {VEHICLE TO VEHICLE COMMUNICATION BY USING RF TECHNOLOGY AND IMAGE PROCESSING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4515-4520},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178297},
        abstract = {Vehicle-to-Vehicle (V2V) communication is a cutting-edge technology that enables vehicles to wirelessly exchange information with one another using RF (Radio Frequency) communication. The primary goal of V2V communication is to enhance road safety, reduce traffic congestion, and improve driving efficiency by allowing vehicles to share real-time data such as speed, position, direction, and braking status. This seamless communication between vehicles helps prevent collisions, enables better traffic flow, and supports autonomous driving features. An important component of V2V communication is object detection, which involves identifying and locating objects such as vehicles, pedestrians, and road signs in digital images or video streams. Object detection relies on computer vision and image processing techniques and is widely used in surveillance, traffic monitoring, and autonomous navigation systems. In this study, object detection is implemented using Python’s OpenCV library along with powerful deep learning algorithms like YOLO (You Only Look Once), SSD (Single Shot Detector), and Faster-RCNN. These models are known for their balance between speed and accuracy, with YOLO being particularly suitable for real-time applications. Deep learning models like Mobile Net are also combined with SSD for lightweight, efficient detection. By integrating V2V communication with advanced object detection, modern vehicles can achieve smarter, safer, and more responsive driving capabilities.},
        keywords = {AObject tracking, OpenCV, computer vision, Webcam, NumPy.},
        month = {May},
        }