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This document presents a drone-based system for wall crack detection using a new dataset called the Ocean University of China Wall Crack Data Set (OUC-Crack), which contains 1,291 images of wall cracks. The proposed Model-Agnostic Meta-Learning Based Segmentation Network (MAML-SegNet) demonstrates improved detection performance with fewer training images, achieving real-time detection capabilities on drones. The study addresses challenges in traditional crack detection methods by leveraging meta-learning te

Author

Shubham ralkar

Language

EN