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What is Semi-Supervised Pavement Anomaly Detection about?

This document presents a semi-supervised learning approach for pixel-level pavement anomaly detection using a network called PAD Net, which leverages generative adversarial networks (GANs). The proposed method effectively identifies anomalous pavement segments without requiring pixel-level annotations, achieving an accuracy of 80.75% on the dataset. The framework includes two generators and three discriminators to maintain background pixels while modifying anomalous regions, addressing challenges in traditi

Author

yogiphd123

Language

EN