  Anomalous crack region detection is a typical binary semantic segmentation
task, which aims to detect pixels representing cracks on pavement surface
images automatically by algorithms. Although existing deep learning-based
methods have achieved outcoming results on specific public pavement datasets,
the performance would deteriorate dramatically on imbalanced datasets. The
input datasets used in such tasks suffer from severely between-class imbalanced
problems, hence, it is a core challenge to obtain a robust performance on
diverse pavement datasets with generic deep learning models. To address this
problem, in this work, we propose a deep learning framework based on
conditional Generative Adversarial Networks (cGANs) for the anomalous crack
region detection tasks at the pixel level. In particular, the proposed
framework containing a cGANs and a novel auxiliary network is developed to
enhance and stabilize the generator's performance under two alternative
training stages, when estimating a multiscale probability feature map from
heterogeneous and imbalanced inputs iteratively. Moreover, several attention
mechanisms and entropy strategies are incorporated into the cGANs architecture
and the auxiliary network separately to mitigate further the performance
deterioration of model training on severely imbalanced datasets. We implement
extensive experiments on six accessible pavement datasets. The experimental
results from both visual and quantitative evaluation show that the proposed
framework can achieve state-of-the-art results on these datasets efficiently
and robustly without acceleration of computation complexity.
