Cracks Segmentation
Hamzah ElQadasi
In my Crack Segmentation project, in partnership with Houston University's structural engineering team, I utilized advanced machine learning techniques to significantly enhance crack detection in images. Through the integration of tools like UNet++, EfficientNet-B3, and PyTorch, I increased pixel detection accuracy from 70% to 86%, improving the effectiveness of damage assessment in structural health monitoring.
Like this project
Posted Mar 2, 2024
The main goal of this project was to enhance the detection and segmentation of cracks in structural images, improving the accuracy of damage assessments.
Likes
0
Views
1
Clients
University of Houston-Data Science Institute
