https://github.com/ravnop/vehicle-speed-and-type-detection-using-yolov8
This system uses artificial intelligence to detect vehicles in video streams, track their movement, and calculate their speeds in real-time. It can: 🚙 Identify different types of vehicles (cars, trucks, buses, motorcycles) 🏎️ Measure vehicle speeds accurately 🚦 Detect and log speed limit violations 📊 Display real-time results on screen
https://github.com/ravnop/vehicle-speed-and-type-detection-using-yolov8
python pythonproject speech-recognition yolov8
Last synced: 6 months ago
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This system uses artificial intelligence to detect vehicles in video streams, track their movement, and calculate their speeds in real-time. It can: 🚙 Identify different types of vehicles (cars, trucks, buses, motorcycles) 🏎️ Measure vehicle speeds accurately 🚦 Detect and log speed limit violations 📊 Display real-time results on screen
- Host: GitHub
- URL: https://github.com/ravnop/vehicle-speed-and-type-detection-using-yolov8
- Owner: RavnOP
- License: mit
- Created: 2025-01-04T08:17:11.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-05T19:19:05.000Z (11 months ago)
- Last Synced: 2025-04-02T12:19:50.877Z (9 months ago)
- Topics: python, pythonproject, speech-recognition, yolov8
- Language: Python
- Homepage:
- Size: 11.3 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚗💨 Vehicle Speed Detection System
🎥 Real-time Traffic Monitoring and Speed Enforcement
# 🌟 What It Does
This system uses artificial intelligence to detect vehicles in video streams,
track their movement, and calculate their speeds in real-time. It can:
🚙 Identify different types of vehicles (cars, trucks, buses, motorcycles)
🏎️ Measure vehicle speeds accurately
🚦 Detect and log speed limit violations
📊 Display real-time results on screen
# 🧩 System Components
Our Vehicle Speed Detection System is composed of several interconnected components,
each playing a crucial role in the overall functionality.
Here's a detailed breakdown of each component:
1. 🎬 Main Application (`main.py`)
🚀 Serves as the entry point for the entire system
🎭 Handles command-line arguments for video source selection
🏁 Initializes and kicks off the video processing pipeline
2. 🎞️ Video Processor (`video_processor.py`)
🧠 Acts as the central brain of the system
🔄 Coordinates interactions between all other components
🖥️ Manages the main processing loop for each video frame
🎚️ Implements performance optimizations like selective frame processing
3. 🚗 Vehicle Detector (`detector.py`)
👁️ Utilizes YOLOv8 AI model for real-time object detection
🔍 Identifies and locates vehicles in each processed frame
🏷️ Classifies detected objects into vehicle categories (e.g., car, truck, bus, motorcycle)
🎯 Applies confidence thresholding to minimize false positives
4. 🖼️ Visualizer (`visualizer.py`)
🎨 Draws bounding boxes around detected vehicles on the video frame
📝 Overlays vehicle information (type, speed) on the video
🚦 Color-codes speed displays based on speed limit compliance
🖥️ Manages the on-screen display of processing statistics (e.g., FPS counter)
5. 📊 Data Logger (`data_logger.py`)
📝 Records speed limit violations in real-time
📅 Timestamps each violation entry
📂 Saves violation data to an Excel file for later analysis
🔒 Ensures data integrity with proper file handling and error management
6. 📹 Video Capture (`video_capture.py`)
🎥 Handles video input from various sources (webcam, video files)
🧵 Implements multi-threading for efficient frame capture
🛠️ Provides fallback options and error handling for different video backends
7. ⏱️ Speed Tracker (`speed_tracker.py`)
📍 Maintains position history for each detected vehicle
🧮 Calculates vehicle speeds based on position changes over time
🔢 Implements algorithms to smooth speed calculations and reduce fluctuations
8. 🧮 Speed Calculator (`speed_calculator.py`)
📐 Converts pixel movements to real-world speed measurements
🧪 Applies statistical methods to filter out anomalies in speed data
🎚️ Adapts calculations based on camera perspective and positioning
9. 📏 Calibration (`calibration.py`)
📐 Handles camera calibration parameters
🗺️ Translates pixel coordinates to real-world distances
🔧 Provides functions to adjust for camera angle and positioning
10. ⚙️ Configuration (`config.py`)
🎛️ Centralizes all system-wide settings and parameters
🚥 Defines speed limits for different vehicle types
🎚️ Allows easy tuning of detection and tracking parameters
🗃️ Stores mappings for vehicle classes and their corresponding indices
# 🛠️ How It Works
1. Video Input: Uses your webcam or a video file
2. Vehicle Detection: Spots vehicles using YOLOv8 AI
3. Tracking: Follows each vehicle across video frames
4. Speed Calculation: Estimates speed based on vehicle movement
5. Visualization: Shows results on screen with bounding boxes and speed info
6. Logging: Records speed violations in an Excel file
# 📋 Requirements
Python 3.8 or newer
OpenCV
NumPy
Ultralytics YOLOv8
openpyxl
SciPy
# 🏃➡️🏃➡️🏃➡️Before Run the Program 🏃➡️🏃➡️🏃➡️:
Install the required packages:
( pip install opencv-python numpy ultralytics openpyxl scipy ) 📏📏OR📏📏 ( pip install -r requirements.txt )
3. Run the Program:
For webcam: `python main.py`
For video file: `python main.py path/to/your/video.mp4`
4. Controls:
Press 'q' to quit
Press 's' to save a screenshot
# ⚙️ Customization
Edit `config.py` to change:
🎯 Detection sensitivity
🏁 Speed calculation settings
🚄 Speed limits for different vehicles
# 📊 What You'll See
- Live video with:
- Boxes around detected vehicles
- Vehicle types (car, truck, etc.)
- Calculated speeds
- Frames per second (FPS) count
- `speed_violations.xlsx` file with logged violations
# ScreeShot......
# 💡 Tips for Best Results
📏 Calibrate the camera settings in `config.py` for your setup
🎥 Use high-quality video input for better accuracy
💻 A computer with a good GPU will run the system faster
# 🔮 Future Plans
- Night-time detection improvements
- License plate recognition
- Web interface for remote monitoring
- Integration with traffic management systems
# 🤝 Want to Contribute?
We welcome your ideas and code contributions! Here's how:
1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to your branch
5. Open a Pull Request
📄 License
This project is a Open Source Project.
👥 Team
[ABHISHEK SINGH](https://github.com/BeingLazyCoder) Project Lead and Creater.
📞 Need Help?
🐛 Issues: [GitHub Issues Page](https://github.com/BeingLazyCoder/Vehicle-Speed-and-Type-Detection-Using-YoloV8/issues)
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# 🌟 Created with passion by [ABHISHEK SINGH] 🌟