About
The software is a tool designed for the automatic analysis and classification of cracks in images. It leverages advanced deep learning techniques and custom algorithms to provide comprehensive insights into the structural integrity of materials.
We employed the pre-trained U-Net model to segment cracks in the images effectively. In addition to segmentation and classification, our software includes a custom algorithm for calculating the angle of the cracks. Understanding the type of crack is crucial for further analysis and decision-making. Our software distinguishes between isolated cracks and map cracks. Isolated cracks are singular, distinct fissures, whereas map cracks form a network of interconnected cracks.
New Contributors
- @roscibely made their first contribution in #1
- @letsticia made their first contribution in #6
- @TIHeitorDS made their first contribution in #7
Full Changelog: https://github.com/cilab-ufersa/crack_detection_app/commits/v.1.0.0
