https://github.com/data-pioneer/surface-crack-detection-yolov8
https://github.com/data-pioneer/surface-crack-detection-yolov8
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/data-pioneer/surface-crack-detection-yolov8
- Owner: data-pioneer
- Created: 2024-01-16T07:17:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-16T09:35:35.000Z (over 1 year ago)
- Last Synced: 2025-01-18T02:14:08.841Z (3 months ago)
- Language: Jupyter Notebook
- Size: 35.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Surface Crack Detection using YOLOv8 and Roboflow
This project implements YOLOv8 for surface crack detection in various materials like concrete, asphalt, and metal. It utilizes a custom dataset downloaded from Roboflow, allowing you to train and deploy a crack detection model tailored to your specific needs.
## Key Features:
##### YOLOv8 for real-time detection: Leverages the accuracy and speed of YOLOv8 for efficient crack identification in images and videos.
##### Custom Roboflow dataset: Train your model on a dataset specifically curated for surface crack detection, improving accuracy and generalizability.Easy training and deployment: Provides Python scripts for training, evaluation, and inference, making it accessible to users with varying technical expertise.
##### Visualization and analysis: Includes tools for visualizing detected cracks and analyzing results to gain insights into crack patterns and severity.
## Getting Started:
##### Clone the repository: Download the project code from GitHub.
##### Install dependencies: Install the necessary Python libraries given at the top of ipynb files.
##### Download the Roboflow dataset: Use the provided Roboflow link or API key to download the custom crack detection dataset. the desired script is provided in Roboflow_crack_model_download.ipynb file.
##### Train the model: Run the crack_detector_Computer_Vision_Project.ipynb script to train the YOLOv8 model on your downloaded dataset.
##### Evaluate the model: after running the scirpt given in above file, yolo video interface will opened with segmentation of detected cracks on surface.
## Contribution:
Feel free to fork this repository, contribute improvements, and share your experiences with the community. We welcome your feedback and suggestions to make this project even more valuable for surface crack detection tasks.