Object Detection Using Machine Learning

DOI

10.2991/978-94-6463-718-2_72How to use a DOI?

Keywords

Object Detection; Machine Learning; Deep Learning; YOLO Framework; Vision Transformers

Abstract

Machine learning-based object detection has become a key technology for automating real-world applications in various fields. We discuss the object detection frameworks, evolution of deep learning models such as YOLOv4, YOLOv6, YOLOv7, and Vision Transformers. They outperform these methods in applications that involve real-time object detection, small object detection and occluded or cluttered environments. The research tackles challenges such as limited labelled data, computational efficiency, and adaptability to diverse scenarios through innovative techniques like unsupervised learning, data augmentation, ensemble methods, and reinforcement learning. In addition, the study emphasizes relevance for context by focusing on industrial use cases, architecture that is expected to scale out, and optimization mechanisms to bridge the gap between academic research and real-world deployable solutions. These advancements work in concert to improve the robustness, accuracy, and efficiency of object detection systems, facilitating their implementation in sectors like surveillance, autonomous systems, and inventory management. The research enhances our knowledge of advanced models and their capability to tackle new problems in object detection.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Keerthana
AU  - V. Vennila
AU  - S. Savitha
AU  - A. Bharathi
AU  - M. Bharathraj
AU  - J. Gowtham
PY  - 2025
DA  - 2025/05/23
TI  - Object Detection Using Machine Learning
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 840
EP  - 851
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_72
DO  - 10.2991/978-94-6463-718-2_72
ID  - Keerthana2025
ER  -