AI-Assisted SmartBrake is an innovative Automatic Braking System that harnesses the power of YOLOv5 and OpenCV to provide advanced object detection and collision avoidance capabilities. This cutting-edge system is designed specially for robots and even enhance road safety and prevent accidents by detecting potential obstacles, pedestrians, and vehicles in real-time.
YOLOv5 Object Detection: Utilizes the state-of-the-art YOLOv5 model for robust and accurate object detection. Real-Time Collision Avoidance: Instantly activates the vehicle's braking system upon detecting potential collisions. High-Performance OpenCV: Leverages the power of OpenCV for efficient image processing and analysis. Customizable Thresholds: Offers adjustable sensitivity levels for personalized safety preferences. Seamless Integration: Easily integrated into autonomous vehicles and robotic applications.
- YOLOv5 Model: The system employs YOLOv5, a deep learning-based object detection model, to identify objects in the vehicle's path.
- Real-Time Detection: Using live video feed from cameras, the system performs real-time object detection for timely decision-making.
- Collision Avoidance: Upon identifying potential obstacles, SmartBrake automatically engages the brakes to prevent collisions.
- Customizable Safety: Users can fine-tune the system's sensitivity to suit different driving conditions.
- Python 3.x
- OpenCV
- PyTorch
- NumPy
- Clone this repository.
- Install the required dependencies
pip3 install torch opencv numpy- Download pre-trained YOLOv5 weights or train your own model.
- Provide the path to the YOLOv5 weights in the code.
- Run the script with the video file.
- View the results.
For more detailed usage instructions and options, refer to the project documentation.
python3 yoloV5_abs.pyContributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.