Real-Time Object Detection Using MobileNet-SSD and OpenCV

Abstract

Object detection is a crucial task in computer vision, finding applications in various fields such as autonomous vehicles, surveillance, and augmented reality. This study explores the implementation of object detection using the OpenCV library, a popular computer vision toolkit. The approach employs a pre-trained deep learning model, specifically the Single Shot MultiBox Detector (SSD), for real-time object detection. The visualization component of the this system utilizes OpenCV’s drawing functions to overlay bounding boxes and labels onto the original frames, providing a visually informative output. The implementation ensures real time performance, making it suitable for applications that demand swift object detection. In addition to real-time video processing, this system demonstrates adaptability to image-based object detection. It accommodates various object classes, making it versatile for diverse use cases. The codebase is modular and well-documented, encouraging extensibility and customization for specific application requirements.

Introduction

Object detection is a key computer vision task that has advanced through powerful models, libraries, and datasets. This study integrates OpenCV, a versatile open-source computer vision library, with the Single Shot Multibox Detector (SSD) model and the COCO dataset to create an accurate, efficient real-time object detection system.

Key Components:

• OpenCV: Provides essential tools for image/video processing and model integration.

• SSD Model: A deep learning architecture that detects objects in a single pass, offering high accuracy and real-time speed by predicting bounding boxes and class labels simultaneously.

• COCO Dataset: A large, diverse dataset containing images with 80 common object categories, enhancing model robustness and real-world applicability.

Literature Insights:
The survey covers various object detection and tracking techniques, including advanced 3D sensing, convolutional and recurrent neural networks, and feature-based tracking algorithms. Challenges like object motion, occlusions, and illumination changes are addressed in prior research, guiding improvements in detection accuracy and efficiency.

Methodology:

• Real-time video frames are captured via webcam or video files using OpenCV.

• The pre-trained MobileNet-SSD model performs detection and classification on each frame.

• Detected objects are labeled and bounded visually in real time, leveraging COCO categories.

• Post-processing filters overlapping detections to refine results.

Results:
The system successfully detects and labels diverse objects in real time, providing clean, user-friendly visual outputs. The combination of OpenCV, SSD, and COCO delivers high accuracy and speed across various scenarios.

References

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Copyright

Copyright © 2025 Prof. Suvarna Thakur, Ashwini Lawande, Atharva Salunkhe, Tanmay Wak, Aniket More, Dr. Shivajirao Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.