Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure

It is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures to extract both fine-grained local features and global crack patterns, significantly enhancing crack detection for improved infrastructure maintenance. Hybrid-Segmentor, trained on a large custom dataset created by merging multiple open-source datasets, can accurately detect cracks on different types of…

• X
  Xenios
• DM
  Deputy Ministry of Research, Innovation and Digital Policy
• H2
  Horizon 2020 Framework Programme
• H2
  Horizon 2020
• HE
  HORIZON EUROPE European Research Council

  Award: 739578