This repository contains code for a self-driving car object detection system using the YOLO (You Only Look Once) algorithm. The system can detect various objects, including cars, in images and video streams from a mounted camera on the car's hood.
The YOLO algorithm is a real-time object detection system that "only looks once" at the image to make predictions. It uses a deep convolutional neural network to detect objects and their bounding boxes in an input image. The model output consists of bounding boxes and class probabilities for each object detected.
To get started with this project, follow these steps:
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Clone this repository to your local machine.
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Install the required dependencies:
- TensorFlow (v2.0 or higher)
- NumPy
- OpenCV
You can install the dependencies using pip: pip install tensorflow numpy opencv-python
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Download the pre-trained YOLO weights from the link provided in the notebooks.
To use the self-driving car object detection system, follow the instructions in the Jupyter notebook provided in the notebooks directory. The notebook guides you through the steps of loading the pre-trained YOLO model, performing object detection on test images, and visualizing the results.
The YOLO algorithm achieves high accuracy in object detection and can run in real-time. The results of object detection on test images and videos demonstrate the effectiveness of the model in identifying cars and other objects.
Contributions to this project are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.