Cpp Object Detection Yolov5 Open CV

C++ Object Detection with YOLOv5 involves implementing real-time and image object detection using the YOLOv5 model in the C++ programming language, enabling identification and localization of objects in images or video streams.

AI & Machine LearningC++MIT

Abstract

Cpp Object Detection Yolov5 Open CV is an open-source AI & Machine Learning project. C++ Object Detection with YOLOv5 involves implementing real-time and image object detection using the YOLOv5 model in the C++ programming language, enabling identification and localization of objects in images or video streams. It is built using C++. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

C++ Object Detection with YOLOv5 involves implementing real-time and image object detection using the YOLOv5 model in the C++ programming language, enabling identification and localization of objects in images or video streams.

Repository consists of two parts. One focuses on object detection from images, and the other on object detection from video media. An ONNX model developed for object detection will be used in both sections

2. Objective

C++ Object Detection with YOLOv5 involves implementing real-time and image object detection using the YOLOv5 model in the C++ programming language, enabling identification and localization of objects in images or video streams.

This project demonstrates how C++ can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

C++

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Arduino IDE / PlatformIO or a C++ compiler (g++)
  • Target board (e.g. Arduino, ESP32) where applicable
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/RsGoksel/Cpp-Object-Detection-Yolov5-OpenCV.git
cd Cpp-Object-Detection-Yolov5-OpenCV

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by RsGoksel and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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