Object Detection Using OpenCV: A Comparative Study of Deep Learning and Traditional Methods

DOI

10.2991/978-94-6463-872-1_62How to use a DOI?

Keywords

OpenCV; YOLO; SSD; Deep-learning; Haar Cascade

Abstract

This study investigates the usage of OpenCV, an Open-Source computer vision toolkit, in Python for object detection. In this paper we use a deep learning-based strategies like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) as well as more classical models like Haar cascades. The effectiveness, precision and real-time performance is assessed on various approaches using benchmark datasets. The results of experiments are how well OpenCV’s deep learning integration and inbuilt feature works for obtaining reliable object detection. The methods for current and future real time object identification systems are explored in this study with the goal of suggesting improvements to the development of real time object identification systems.

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  - Kartikey Kumar Pal
AU  - Surya Kant Pal
AU  - Saloni Srivastava
AU  - Hari Shankar Shyam
AU  - Sachin Singh
PY  - 2025
DA  - 2025/11/04
TI  - Object Detection Using OpenCV: A Comparative Study of Deep Learning and Traditional Methods
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 1018
EP  - 1027
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_62
DO  - 10.2991/978-94-6463-872-1_62
ID  - Pal2025
ER  -