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.
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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.
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Clients
University of Houston-Data Science Institute
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