---

Universal Object Detector

---

Project Description

UniversalObjectDetector is a flexible and high-performance object detection framework that supports multiple detection techniques, including Haar Cascades, YOLO (You Only Look Once), and custom Machine Learning models. Designed for both images and real-time video streams, it provides precise and efficient object recognition while maintaining a modular structure for easy extension and optimization.

This project is divided into three main components:

• Core: Defines object models, interfaces, and essential utility functions.
• Detectors: Implements various detection algorithms, making it easy to integrate new models.
• App: A console-based application demonstrating real-time object detection.

---

Features

• Multi-Model Support
• – Integrates Haar Cascades, YOLO, and ML-based detection.
• Real-Time Processing
• – Detects objects in live video feeds from webcams.
• Image-Based Detection
• – Works with static images as well as video streams.
• ⚙Modular and Extensible
• – Easily add new detection methods and configurations.
• Configurable Resources
• – Supports external model files, configuration settings, and datasets.

---

Project Structure

UniversalObjectDetector/
├── UniversalObjectDetector.Core/
│   ├── Models/
│   │   ├── DetectedObject.cs
│   │   ├── ObjectCategory.cs
│   ├── Interfaces/
│   │   ├── IObjectDetector.cs
│   ├── Utilities/
│       ├── ImageProcessingHelpers.cs
│       ├── ConfigurationManager.cs
│
├── UniversalObjectDetector.Detectors/
│   ├── Implementations/
│   │   ├── HaarCascadeDetector.cs
│   │   ├── YOLODetector.cs
│   │   ├── MachineLearningDetector.cs
│   ├── Config/
│   │   ├── DetectorConfiguration.cs
│   ├── Utilities/
│       ├── DetectionHelpers.cs
│
├── UniversalObjectDetector.App/
│   ├── Program.cs
│
├── resources/
│   ├── cascades/           # Pre-trained Haar cascade XML files
│   ├── models/             # YOLO and ML model weights/configs
│   ├── training_images/    # Datasets for training/testing
│   ├── config.json         # Configuration settings
│
└── README.md

---

Dependencies

Ensure the following NuGet packages are installed:

OpenCvSharp4
OpenCvSharp4.runtime.win
System.Drawing.Common

---

Setup and Usage

1️⃣ Clone the Repository

git clone https://github.com/your-username/UniversalObjectDetector.git
cd UniversalObjectDetector

2️⃣ Build and Run

1. Open the project in Visual Studio.
2. Restore NuGet dependencies:

   dotnet restore

3. Run the console application:

   dotnet run --project UniversalObjectDetector.App

3️⃣ Configuration

• Place Haar cascade XML files in resources/cascades/.
• Store YOLO model files (.cfg, .weights) in resources/models/.
• Edit config.json to switch between different detection methods.

---

License

This project is licensed under the MIT License – feel free to use and modify it.

---

Contributing

Contributions are welcome! If you’d like to enhance the project, submit an issue or create a pull request.

---

Contact

For any inquiries, reach out via GitHub Issues.

---

This version makes the project more engaging, readable, and structured while keeping everything informative. Let me know if you want any tweaks! 🚀