Abstract
Real-time object detection has emerged as a critical application in computer vision, enabling intelligent systems to perceive and interact with their environment. This study presents a comparative analysis of lightweight deep learning models, namely MobileNet-SSD and YOLO-based architectures, for real-time object detection using OpenCV and Python. The proposed system processes live video streams by leveraging OpenCV’s DNN module to perform efficient inference on CPU hardware. The performance of the models is evaluated based on frame processing speed (FPS), detection accuracy, and computational efficiency. Experimental results demonstrate that MobileNet-SSD achieves higher processing speed, making it suitable for resource-constrained environments, while YOLO-based models provide improved detection accuracy. The study highlights the trade-off between speed and accuracy and provides insights into selecting appropriate models for real-time applications such as surveillance, automation, and smart monitoring systems.
Citations
IRE Journals:
Himanshu Rai, Vansh Khatana, Dr. Mohd Danish "Implementation of Object Detection using OpenCV and Python" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2048-2050 https://doi.org/10.64388/IREV9I10-1716506
IEEE:
Himanshu Rai, Vansh Khatana, Dr. Mohd Danish
"Implementation of Object Detection using OpenCV and Python" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716506
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12