khanhha/crack_segmentation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
Implements semantic segmentation for pavement and concrete cracks using encoder-decoder UNet architectures with VGG16 and ResNet101 backbones for transfer learning. The solution aggregates 11,200 images from 12 public crack datasets into a unified 448×448 benchmark, trained with PyTorch and evaluated using IoU and Dice metrics. Addresses real-world robustness through diverse test scenarios including moss occlusion, texture noise, and large contextual backgrounds to reduce false positives.
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May 06, 2024
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