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:
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.