Smart Classroom Behavior Detection using YOLOv8
Internship Project Organization:Bharat Electronics Limited (BEL)
Project Overview
This project was developed during my internship at Bharat Electronics Limited (BEL). The main objective of the project is to detect different student behaviors in a classroom using the YOLOv8 object detection model. The system analyzes classroom images and videos and identifies various student activities, making it useful for classroom monitoring and behavior analysis.
Project Objectives
- Detect different student behaviors using Computer Vision.
- Train a custom YOLOv8 model on a classroom dataset.
- Test the model on classroom images and videos.
- Analyze the detection results using evaluation metrics.
- Understand the workflow involved in an AI-based object detection project.
Technologies Used
- Python
- YOLOv8
- OpenCV
- Google Colab
- Roboflow
- Kaggle Dataset
- GitHub
Dataset
The project uses a classroom behavior dataset collected from Kaggle. The dataset was annotated and exported using Roboflow in YOLOv8 format before training the model.
Project Workflow
Dataset Collection ↓ Data Preprocessing ↓ YOLOv8 Model Training ↓ Model Validation ↓ Image & Video Testing ↓ Behavior Detection
Behaviors Detected
The model detects the following classroom activities:
- Hand Raise
- Reading
- Writing
- Sleeping
- Using Device
- Standing
- Looking Forward
- Turning Head
Results
The trained model was able to detect multiple classroom behaviors successfully. The project includes:
- Prediction Images
- Video Detection
- Confusion Matrix
- Precision Curve
- Recall Curve
- Precision-Recall Curve
- Training Results Graph
Repository Structure
📁 documentation
📁 model
📁 notebooks
📁 outputs
📁 screenshots
README.md
requirements.txt
Future Improvements
- Improve detection accuracy using a larger dataset.
- Implement real-time webcam detection.
- Develop a classroom analytics dashboard.
- Generate attendance and engagement reports.
- Deploy the project as a web application.
Author
B S Vanisha Babu
B.Tech Computer Science Engineering (AI & ML)
Presidency University, Bengaluru
Project Status: Completed