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YOLO v8 Cheating Detection System 📊

June 15, 2024 (7mo ago)

🚀 A real-time cheating detection system powered by YOLO v8, designed to monitor and flag cheating behaviors in various scenarios like exams or games.

💻 Source Code  • 
YOLO v8 Cheating Detection System: Real-Time Monitoring
YOLO v8 Cheating Detection System: Real-Time Monitoring

📍 Abstract

The YOLO v8 Cheating Detection System leverages the YOLO (You Only Look Once) v8 deep learning algorithm for real-time object detection, specifically tailored to detect cheating behaviors. It can be applied in environments like exams, games, or any scenario where cheating monitoring is essential. The system utilizes YOLO's efficient object detection capabilities to flag suspicious activities with bounding boxes and confidence scores.

🌟 Features

  • Real-Time Cheating Detection: Monitors and flags suspicious behaviors.
  • YOLO v8 Object Detection: Leverages the advanced YOLO v8 model for real-time performance.
  • Confidence Scores: Object detection results come with confidence scores for accuracy.
  • Customizable Parameters: Easily adjustable confidence and suppression thresholds.
  • Ease of Use: Simply run the Python script after setup to start detecting.

🚀 Getting Started

Prerequisites

  • Python 3.x
  • OpenCV (cv2)
  • NumPy

Installation

  1. Clone the repository:
git clone https://github.com/verus56/Cheating-Detection-System.git  
cd yolo-cheating-detection-system
  1. Install required dependencies:
pip install opencv-python numpy

  1. Download the YOLO v8 model configuration (model.cfg) and pre-trained weights (model.weights) from the official YOLO website or trusted sources.

  2. Create a labels.names file with class labels corresponding to the model.

  3. Place the model.cfg, model.weights, and labels.names files in the project directory.

Running the App

python object_detection.py

The application will process the video and display the detected objects with bounding boxes and confidence scores.

🤖 How It Works

  1. Modify the video_path variable in the script to specify the video you want to analyze.
  2. Run the detection script to start monitoring for cheating behaviors.
  3. Adjust parameters like minconf and minthresh for optimal performance.

📊 Technical Stack

  • AI Engine: YOLO v8
  • Image Processing: OpenCV
  • Data Processing: NumPy
  • Object Detection: YOLO

🛠️ Deployment

The system can be run locally or deployed on cloud platforms for real-time surveillance.

📝 License

Released under the MIT License.

📲 Contact

Made with ❤️ by Hamzaoui Thameur