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@article{191811,
        author = {Kuruba Pundepalle Tirumalesh and Avvar Venkat Vinay and Gorantla Charan Ranga and Maitri Veeramani and Pateghar Mohammed Owais and C Rambabu and Dr. C V Madhusudan Reddy},
        title = {REAL TIME OBJECT DETECTION USING YOLO},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7882-7887},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191811},
        abstract = {The Objective is to detect of objects using You Only Look Once (YOLO) approach. This method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network, Fast Convolutional Neural Network the algorithm will not look at the image completely but in YOLO the algorithm looks the image completely by predicting the bounding boxes using convolutional network and the class probabilities for this box. In the realm of computer vision, object detection poses a significant challenge, especially in accurately localizing and categorizing objects within images. This paper introduces a novel method that utilizes a pretrained deep learning model to detect objects in real time by processing webcam-captured images and video streams. Leveraging the renowned speed and accuracy of the YOLO algorithms, the system efficiently counts and monitors specific objects in each video frame, making it especially apt for visualization-focused applications. By integrating the YOLO model with the COCO dataset, the approach outperforms existing techniques in terms of precision and real-time responsiveness.},
        keywords = {Computer Vision, Object Detection, YOLO algorithm, Deep Learning, Open-CV, COCO dataset, CNN.},
        month = {January},
        }