Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194746,
        author = {Umamaheswararao Mogili},
        title = {High Speed Real-Time Object Detection Using YOLO-Based Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6541-6546},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194746},
        abstract = {Real-time object detection is an important research area in the field of Computer Vision that focuses on identifying and locating objects in images and video streams with minimal delay. With the rapid advancement of Artificial Intelligence and deep learning technologies, object detection systems have become increasingly accurate and efficient. These systems are widely used in various applications such as surveillance systems, autonomous vehicles, robotics, healthcare monitoring, smart city infrastructure, and assistive technologies for visually impaired individuals. The ability to detect objects in real time enables intelligent systems to respond quickly to changes in their environment. In recent years, deep learning-based detection algorithms have significantly improved the performance of object detection systems. One of the most popular algorithms is the You Only Look Once (YOLO) model, which performs object detection using a single neural network. Unlike traditional object detection techniques that require multiple processing stages, YOLO predicts bounding boxes and object class probabilities simultaneously in a single pass through the network. This approach greatly reduces computation time and enables real-time processing. The proposed system in this research utilizes the YOLO-based deep learning model to detect objects from live video streams. The system captures video input using a camera and processes the captured frames continuously. Each frame is analyzed by the trained YOLO model, which identifies objects present in the scene and draws bounding boxes around them. The detected objects are then labeled with their corresponding class names, such as person, car, bottle, chair, and other commonly recognized objects. The system is implemented using the Python programming language along with the OpenCV computer vision library. Python provides a flexible programming environment for integrating machine learning models, while OpenCV is used for capturing video frames, processing images, and displaying the detection results in real time. The combination of Python and OpenCV allows the system to process visual data efficiently and supports the implementation of advanced computer vision techniques.},
        keywords = {Object Detection, YOLO, Deep Learning, Real-Time Systems, Computer Vision, Text-to-Speech.},
        month = {March},
        }