Pothole Detection Using YOLOv8

Pothole Detection is a deep-learning-based application that identifies road damage, particularly potholes, using YOLOv8. This project aims to assist in road maintenance and autonomous driving by providing an efficient way to detect and classify potholes.

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Features

✅ Real-time pothole detection using YOLOv8
✅ Lightweight and efficient model
✅ User-friendly GUI built with PyQt
✅ Supports image input

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Download & Run the Application

You can download the latest version of the application from GitHub Releases:

Download Here

How to Run the Application (Windows)

1. Download the .exe file from the link above.
2. Double-click the .exe file to launch the application.
3. Load an image, and the model will detect potholes in real time.

(No need to install Python or dependencies!)

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Example Outputs

Input Image	Detection Output

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Model Details

• Model: YOLOv8 (trained on a custom dataset for pothole detection)
• Framework: PyTorch
• Dataset: Annotated road damage images from various sources on Roboflow
• Accuracy of 81.65%, precision of 81.40% and recall of 81.89%.

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Contact

If you have any issues or suggestions, feel free to open an issue or reach out.