Generative Adversarial Networks for Binary Semantic Segmentation on Imbalanced Pavement Datasets

The author proposes a deep learning framework based on conditional Generative Adversarial Networks (cGANs) to address the challenge of anomalous crack region detection in pavement images. The approach involves incorporating attention mechanisms and entropy strategies to enhance model performance on imbalanced datasets.