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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Real-Time Object Detection Using YOLO |
| Authors Name: | Kanishkaa K , Mithun R , Ramani R , Santhoshi P |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_202044
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| Published Paper Id: | IJRTI2504044 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Real-time object detection is a crucial task in computer vision that enables the identification and localization of objects in images and video frames. It is widely used in applications such as autonomous driving, surveillance, traffic monitoring, and robotics, where real-time processing is essential. The You Only Look Once (YOLO) model is a state-of-the-art deep learning approach known for its speed and accuracy. Unlike traditional region-based methods, YOLO processes an entire image in a single pass using a fully convolutional network, dividing it into a grid and predicting bounding boxes and class probabilities simultaneously, making it highly efficient for real-world applications. YOLOv7, the latest iteration, offers improved accuracy, faster inference, and better model efficiency. In this paper, we propose a customized YOLOv7-based real-time object detection model with various enhancements aimed at improving detection precision, computational efficiency, and adaptability for public utility. Our approach integrates advanced pre-processing, optimized training strategies, and fine-tuned hyper-parameters to enhance object recognition while ensuring real-time performance. Additionally, we incorporate techniques such as model quantization and pruning to optimize speed and reduce computational costs. The proposed model is well-suited for applications requiring high-speed and accurate detection, contributing to smart cities, surveillance, and traffic monitoring, making it a valuable asset in modern technological advancements. |
| Keywords: | Real-time object detection, YOLOv7, deep learning, computer vision, bounding box prediction, surveillance, traffic monitoring, smart cities, real-time processing. |
| Cite Article: | "Real-Time Object Detection Using YOLO", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a298-a306, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504044.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504044
Registration ID:202044
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a298-a306 Country: Coimbatore, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504044 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504044 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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