mrFahrenhiet/CrackSegmentationDeepLearning
Multiscale Attention Based Efficient U-Net for Crack Segmentation, segments a RGB image into 2 classes crack and non-crack, this method obtained SOTA results on Crack500 dataset
This project helps civil engineers and maintenance crews automatically identify and monitor cracks in structures like pavements. It takes an RGB image of a surface and outputs a segmented image highlighting areas identified as cracks, distinguishing them from non-crack areas. This tool is designed for professionals involved in structural health monitoring and infrastructure inspection.
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Use this if you need to precisely locate and map cracks within images for structural assessment and maintenance planning.
Not ideal if you need to detect other types of structural damage beyond cracks, or if your images are not clear enough to differentiate surface features.
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Language
Python
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Last pushed
Oct 15, 2022
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