Image detection using YOLO-based object detection models

Edim Bassey Edim; Akpan Itoro Udofot

doi:10.30574/ijsra.2025.14.3.0467

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Image detection using YOLO-based object detection models

Edim Bassey Edim ^{1, *} and Akpan Itoro Udofot ²

¹Department of Computer Science, Faculty of Physical Science, University of Calabar, Cross River State Nigeria.

²Department of Computer Science Federal School of Statistics Amechi Uno, Awkunanaw, Enugu, Enugu State, Nigeria.

Review Article

International Journal of Science and Research Archive, 2025, 14(03), 944-959

Article DOI: 10.30574/ijsra.2025.14.3.0467

DOI url: https://doi.org/10.30574/ijsra.2025.14.3.0467

Publication history

Received on 28 January 2025; revised on 04 March 2025; accepted on 06 March 2025

Abstract

Object detection is a core aspect of computer vision, enabling precise identification and localization of objects in images and videos. YOLO (You Only Look Once) revolutionized the field by framing object detection as a regression problem, using a single convolutional neural network for real-time detection. Combining speed, accuracy, and simplicity, YOLO has significantly impacted applications like autonomous driving, surveillance, and medical imaging. This journal reviews YOLO's architecture, evolution, applications, and challenges, highlighting its contributions to artificial intelligence and computer vision.

Keywords

Image; Detection; YOLO-Based Object; Detection; Models

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How to cite this article

Edim Bassey Edim and Akpan Itoro Udofot. Image detection using YOLO-based object detection models. International Journal of Science and Research Archive, 2025, 14(03), 944-959. Article DOI: https://doi.org/10.30574/ijsra.2025.14.3.0467.

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