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license: apache-2.0
tags:
- vision
- object-detection
- yolo
- crack-detection
- infrastructure
- civil-engineering
datasets:
- custom
metrics:
- mAP
- precision
- recall
---
# Crack Detection YOLO Model
This repository contains a YOLO-based object detection model specifically trained to identify and localize cracks in various infrastructure surfaces, including concrete walls, floors, and brick facades.
## Model Overview
- **Task**: Object Detection
- **Class**: `crack`
- **Architecture**: YOLO (Ultralytics)
- **Training Epochs**: 100
- **Input Resolution**: 640x640 (standard YOLO inference)
## Performance & Training Graphics
The following graphics demonstrate the model's training progression and final performance metrics.
### Training Results
*The plots show consistent convergence in both training and validation losses (Box, Cls, DFL) over 100 epochs, with precision and recall stabilizing at high levels.*
### Precision-Confidence Curve
*The model achieves a precision of 1.00 at a confidence threshold of 0.987, indicating very high reliability in detections at high confidence levels.*
## Inference Examples
The model has been validated on diverse surfaces showing robust detection capabilities.
### Concrete Surface Detection
### Building & Brick Wall Detection
## Usage
To use this model with the Ultralytics YOLOv8 library:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('crack.pt')
# Perform inference on an image
results = model.predict('path/to/your/image.jpg', save=True, conf=0.5)
# View results
for result in results:
result.show()
```
## Dataset Information
The model was trained on a specialized dataset focused on structural cracks. It includes variations in lighting, surface textures, and crack sizes to ensure better generalization in real-world infrastructure inspections.
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