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Utbird/
crack_detection

Name: crack_detection
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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.

Utbird/crack_detection