Road Sign Detection Algorithm using OpenCV

Introduction

The primary objective of this project is to develop an algorithm capable of accurately detecting road signs within images. Accurate road sign detection is crucial in various applications, including autonomous driving, as it ensures the safety and functionality of the system.

Image Preprocessing

Initial Image Processing

• Image Loading: The project begins by reading input images using OpenCV's imread function. These images are typically in RGB format, which introduces complexity into the detection process.

• Grayscale Conversion: To simplify the detection process, the RGB color model is converted to grayscale using OpenCV's cvtColor function. This transformation facilitates more efficient and precise analysis of the images.

Edge Detection

Canny Edge Detection

• Edge Detection Algorithm: The Canny edge detection algorithm is employed to identify the edges of objects within the images. It operates on the grayscale image generated during the preprocessing stage.

• Parameter Fine-Tuning: To achieve optimal results, the parameters for the Canny edge detection function are fine-tuned. These adjustments aim to strike the right balance between sensitivity and noise reduction, allowing for a more accurate edge detection process.

Circle Detection

Hough Circle Transform

• Circle Detection Algorithm: The Hough Circle Transform is utilized to detect circular objects within the images, which correspond to road signs. Detected circles are outlined and filled in green to highlight their presence.

• Parameter Optimization: Similar to edge detection, parameters for the Hough Circle Transform are fine-tuned to maximize detection accuracy. These parameters are adjusted to achieve the most reliable results in detecting road signs.

Road Sign Detection

• The road signs that are detected within the images are highlighted in GREEN color in the final output image. This visual identification makes it easy to recognize the detected road signs.

Conclusion

In conclusion, the algorithm developed in this project demonstrates its effectiveness in detecting road signs within images. It employs various image processing techniques, including the Canny edge detection algorithm and the Hough Circle Transform. The fine-tuning of parameters across different functions ensures the algorithm delivers the best possible results. This algorithm has potential applications in various domains, particularly in the context of autonomous driving and computer vision.

Usage

• Prerequisites: Ensure you have OpenCV installed in your development environment. If not, you can follow the installation instructions on the OpenCV website.

• Clone the Repository: Clone this repository to your local machine.

• Compilation: Compile the C++ code provided with OpenCV support. Make sure to include the necessary OpenCV headers and links to the OpenCV libraries.

• Execution: Run the compiled executable, providing your own input image for road sign detection. The output image will display the detected road signs in green.

Contributions

Contributions to this project are highly encouraged. If you have suggestions, enhancements, or fixes, please feel free to create pull requests. Collaborative efforts can help improve the algorithm's accuracy and broaden its applications.

License

This project is licensed under the MIT License. You can find the full license details in the LICENSE file within the repository.

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