  Automated pavement crack image segmentation is challenging because of
inherent irregular patterns, lighting conditions, and noise in images.
Conventional approaches require a substantial amount of feature engineering to
differentiate crack regions from non-affected regions. In this paper, we
propose a deep learning technique based on a convolutional neural network to
perform segmentation tasks on pavement crack images. Our approach requires
minimal feature engineering compared to other machine learning techniques. We
propose a U-Net-based network architecture in which we replace the encoder with
a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule
based on cyclical learning rates to speed up the convergence. Our method
achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset,
outperforming other algorithms tested on these datasets. We perform ablation
studies on various techniques that helped us get marginal performance boosts,
i.e., the addition of spatial and channel squeeze and excitation (SCSE)
modules, training with gradually increasing image sizes, and training various
neural network layers with different learning rates.
